Data processing method and related devices

Abstract

This application provides a data processing method relating to the field of media applications. The method includes the steps of: acquiring a media asset, wherein the media asset includes media content, and further includes at least one of metadata for the media content and a trust record for the media content; and determining one or more trust metrics for the media asset, wherein the one or more trust metrics are used to evaluate the reliability level of the media asset. The method enables the evaluation of the reliability level of a media asset by extracting trust metrics, thereby eliminating the need to directly read the media asset during subsequent evaluations of the reliability level, and instead relying solely on the information read from the trust metrics to evaluate the reliability level of the media asset.

Description

Technical Field

[0001] This Disclosure relates to the fields of terminals and media applications, and particularly to data processing methods and related devices.

Background Art

[0002] The spread of more convenient imaging devices such as mobile phones, combined with the development of various types of simple, efficient, and powerful content editing software (e.g., AIGC software), has reduced the barriers to media creation and modification for people and enabled publication across various social media platforms. However, the modification of media content exacerbates the spread of some misinformation and false information, damaging the reliability of media content as an information carrier. In particular, in light of the progress of AI, it has become increasingly difficult for the naked eye to distinguish between processed fake content and real content, and "seeing" does not necessarily lead to "believing." Therefore, a technology that can improve the ability to evaluate the reliability level of content is important.

[0003] For example, the media content is an image. In existing implementation forms, in order to detect whether media content contains fake content, an AI detection model can be trained on a large number of real image sets and fake image sets. The detection model detects whether the input image has features similar to those of fake images to determine whether the image is reliable. However, this type of AI detection model can only verify the reliability levels of some specific fake content.

[0004] Therefore, a method that can verify the reliability level of media content is urgently needed.

Summary of the Invention

[0005] According to a first aspect, the present application provides a data processing method which obtains reliability information of a media asset by using one or more reliability indicators of the media asset. handle Metric Requirements satisfies This includes a step to determine whether or not. Users can determine the reliability level of a media asset based on reliability information. A media asset includes media content. A media asset further includes at least one of the following: metadata for the media content and a reliability record for the media content. A reliability metric is a parameter that indicates the reliability level of a media asset.

[0006] In possible implementations, the method further includes the step of generating reliability information for media assets.

[0007] In possible implementations, reliability information can be encapsulated in a reliability report.

[0008] In possible implementations, one or more trust metrics are determined based on at least one of the following: media content, metadata corresponding to the media content, and trust records corresponding to the media content. For example, the trust metrics for a media asset may be features extracted from the media content, metadata corresponding to the media content, and trust records corresponding to the media content, or they may be data obtained by performing specific processing on the media content, metadata corresponding to the media content, and trust records corresponding to the media content.

[0009] In possible implementations, reliability information indicates whether one or more reliability metrics meet the metric, and the reliability metrics include parameters that indicate the reliability level of the media asset. For example, if the media content is an image, features such as the time of shooting, location of shooting, photographer, camera lens parameters, and shooting parameters in the metadata may be selected as reliability metrics for the metadata.

[0010] For example, if the media content is an image, the image generation method, image editing method, etc., within the trust record may be selected as the trust indicator for the trust record. The image generation method is the media content AIGC software This may include, but is not limited to, whether the media content is generated via a camera, whether the media content is generated using software assistance, whether the media content is generated via synthetic media, and whether the media content can be used to train a model. Image editing methods may include, but are not limited to, rotation, resizing, cropping, and content editing.

[0011] For example, media content may be an image, and features such as people, scenes, object types, spatial relationships between objects, or image style (e.g., color, lighting, or brightness) within the media content may be selected as indicators of the media content's reliability.

[0012] In possible implementations, metrics corresponding to the trust indicators are defined within the profile, and first trust configuration information may be obtained, which includes metrics that the media asset must satisfy.

[0013] In possible implementations, one or more trust metrics for a media asset may be determined based on at least one of the following: media content, metadata corresponding to the media content, and trust records corresponding to the media content.

[0014] In possible implementations, one or more confidence metrics can be encapsulated in a confidence certificate.

[0015] In possible implementations, the method further includes the step of adding confidence information to the confidence record, obtained by determining whether each confidence metric meets the metric.

[0016] By adding newly generated reliability information to the media asset's reliability record, the reliability of the media asset can be further enhanced.

[0017] In possible implementations, the step of determining whether one or more confidence metrics satisfy a metric includes the step of determining whether a confidence metric in the metric is included in one or more confidence metrics, or whether one or more confidence metrics include a confidence metric in the metric.

[0018] In other words, the metric may also include confidence metrics, and whether the confidence metrics of a media asset are included in the metric (i.e., whether the metric includes confidence metrics of a media asset) may be determined in order to determine whether the confidence metrics of a media asset satisfy the metric.

[0019] In possible implementations, a trust record includes a trust manifest, and the step of adding trust information to a trust record includes the step of adding trust information to a trust manifest.

[0020] In possible implementations, the method further includes the step of adding the hash value and signature of the trustworthiness information to the trustworthiness manifest.

[0021] In possible implementations, the method further includes the step of obtaining reference information for a media asset, and the step of adding to a trust record includes the step of adding trust information to the trust record corresponding to the media asset indicated by the reference information, based on the reference information. If the trust information for multiple media assets needs to be processed in batches, the reference information for the media assets may be transmitted in the process.

[0022] In possible implementations, the method further includes the step of generating files associated with the media asset based on reliability information.

[0023] In a possible implementation form, the first trust configuration information includes one of a plurality of trust configuration information, and different trust configuration information indicates different area or user metric requirements that the media asset needs to meet. Different scenarios (e.g., areas or users) may correspond to different requirements, and different pluralities of trust information may be generated, so that the trust evaluation requirements for different scenarios can be satisfied.

[0024] In a possible implementation form, the operation of obtaining one or more trust indicators of a media asset is triggered by capturing media content via a hardware sensor or generating media content via generation software.

[0025] For example, an image may be captured via a camera built into a terminal device, or new media content may be generated via media content editing software (e.g., AIGC). When obtaining media content, the terminal device may further need to generate a corresponding media asset via a media asset management module, and the media asset may include metadata, trust records, etc. In this embodiment of the present application, the media asset management module may capture media content via a hardware sensor or generate media content via generation software, generate trust information based on the content in the media asset, and add the trust information to the media asset.

[0026] In a possible implementation form, before obtaining one or more trust indicators of a media asset, the method further includes the step of receiving a trust evaluation requirement for the media content.

[0027] For example, the user may input a trust evaluation requirement for the media content via a trust evaluation application.

[0028] For example, on the client side, a reliability evaluation request for media content may be input via an interface of a cloud service for providing a reliability evaluation service.

[0029] In a possible implementation form, the media content is at least one of an image, a video, or an audio.

[0030] According to a second aspect, the present application provides a data processing method, the method including: a step of obtaining a media asset, where the media asset includes media content and further includes at least one of metadata of the media content and a trust record of the media content; and a step of determining one or more trust indicators, where the one or more trust indicators are used to evaluate the reliability level of the media asset.

[0031] This method enables the reliability level of a media asset to be evaluated by extracting trust indicators, thereby eliminating the need to directly read the media asset during subsequent evaluations of the reliability level. Instead, it depends only on the information read from the trust indicators to evaluate the reliability level of the media asset.

[0032] In a possible implementation form, the one or more trust indicators are encapsulated in a trust certificate.

[0033] In a possible implementation form, the one or more trust indicators are determined based on at least one of the media content, the metadata corresponding to the media content, and the trust record corresponding to the media content.

[0034] In possible implementations, one or more reliability indicators TM related to the reliability level of a media asset may be determined based on the metadata of the media content. For example, TM may include the time the media content was created, the name of the creator of the media content, the digital content identifier of the media content, the location where the media content was created, information about the device used to create the media content, the media type of the media content, or the method of creating the media content. For example, information about the device used to create the media content may include the device model, camera parameters, etc., and camera parameters may include the camera's focal length, sensitivity, exposure, etc., during photography. It should be understood that TM may further include other information, such as copyright information, which is not limited to this application.

[0035] In possible implementations, one or more trust metrics (TRs) related to the reliability level of a media asset may be determined based on the media content's trust record. For example, the production time of the media content, the name of the media content's creator, the media content's digital content identifier, the media content's production location, information about the media content's production device, the media editing method, the media type of the media content, or the media content's production method may be selected as TRs. For example, information about the media content's production device may include the device model, camera parameters, etc., and camera parameters may include the camera's focal length, sensitivity, exposure, etc., during photography. It should be understood that TRs may further include other information, such as copyright information, used to describe the media content. This is not limited to this application. The media production method may include, but is not limited to, whether the media content is produced via AIGC, whether the media content is produced via a camera, whether the media content is produced using software assistance, whether the media content is produced via synthetic media, and whether the media content can be used to train a model. The media editing method may include, but is not limited to, rotation, resizing, cropping, content editing, etc. The TR may further include media assertions, which may include thumbnails, whether modifications are permitted, permission to operate, permission to use, and the source of secondary media content.

[0036] In possible implementations, one or more confidence metrics (TC) related to the reliability level of a media asset may be determined based on the media content. For example, features such as people, scenes, object types, spatial relationships between objects, and media style (e.g., color, lighting, or brightness) within the media content may be selected as confidence metrics for the media content.

[0037] In possible implementations, one or more confidence metrics are used to determine the reliability information of a media asset, and this reliability information indicates whether one or more confidence metrics meet the metric.

[0038] In possible implementations, metrics include data indicating the reliability indicators that the media asset must meet. For example, a metric may indicate the creator, and the metric may be the creator or the creator's name. For example, a metric may indicate the media generation location, and the metric may be the location or the location's name. Examples are not listed herein.

[0039] In possible implementations, the reliability metric includes parameters that indicate the reliability level of the media asset.

[0040] In possible implementations, media content is at least one of the following: images, videos, or audio.

[0041] According to a third aspect, the application provides a data processing method comprising the steps of acquiring first media content and generating a trust record of the first media content, wherein the trust record of the first media content comprises initial information of the first media content, hard binding of the first media content, and a first digital signature, wherein the first digital signature is a digital signature of first data, and the first data is data determined based on at least the initial information and hard binding of the first media content. The trust record can be used to track the origin of media content and can help a user determine to some extent the authenticity of media content.

[0042] In possible implementations, the first data is data obtained by combining at least a hard binding of the first media content with initial information of the first media content, or the first data is at least a hard binding of the initial information (for example)、A This data is obtained by combining the hash value of the IGC (AIGC.Hash, or the hash value of the media generation method, Media Type.Hash) with the hardbinding of the first media hardbinding (for example, the hash value of the first media hash value, Media Hash 0.Hash).

[0043] In possible implementations, the first data may alternatively be a first combined hash value (Hash(TD)), which includes a hash value obtained by combining the initial information of the first media content with the first media hash value. For example, the first combined hash value includes a hash value obtained by combining the initial information of the first media content, the first media hash value, and other descriptive information, the other descriptive information not having to be other descriptive information within the initial information, but could be, for example, information about a tool for generating a trust profile, or other information describing the initial information of the first media content.

[0044] In possible implementations, the first data may alternatively be a first combined hash value, which may include a hash value obtained by combining the hash value of the initial information of the first media content and the hash value of the first media hash value (Media Hash 0. Hash). For example, the first combined hash value is a hash value obtained by combining the hash value of the initial information of the first media content, the hash value of the first media hash value, and the hash values ​​of other metadata information. Here, if there are at least two initial information items for the first media content, the hash value of the initial information of the first media content may include the hash value of each initial information item for the first media content, or it may include a hash value obtained by combining all of the initial information items for the first media content.

[0045] In possible implementations, initial information for media content may be information generated when the media content is created. For example, initial information for media content may include at least one of the following: the time the media content was created, the name of the creator of the media content, the digital content identifier of the media content, the location where the media content was created, information about the device that created the media content, the resolution of the media content, the size of the media content, the media type of the media content, or the method of creating the media content. For example, information about the device that created the media content may include the device model, camera parameters, etc., and camera parameters may include the camera's focal length, sensitivity, exposure, etc., during photography. It should be understood that initial information for media content may further include other information, such as copyright information, which is used to describe the media content.

[0046] In possible implementations, the media type may include, but is not limited to, image type, video type, audio type, graphics type, etc. The generation method may include AI generation method, non-AI generation method, etc. In possible forms, the generation method in the initial information may be replaced with an AI generation (AIGC) identifier. For example, if the media content is generated by AI, the value of the AIGC identifier is 1, and if the media content is not generated by AI, the value of the AIGC identifier ID is 0. The media content creator name may be the name of the creator of the media content or the name of the device used to capture the media content.

[0047] According to a fourth aspect, the present application provides a data processing device, the device including: An acquisition module configured to acquire one or more trust metrics for a media asset, wherein the media asset includes media content, metadata corresponding to the media content, and a trust record corresponding to the media content, and A processing module configured to determine whether one or more confidence metrics meet the metric.

[0048] In possible implementations, the acquisition module is: Obtain the first trust configuration information. Further configured to perform this, the first trust configuration information includes metrics that the media asset must meet.

[0049] In possible implementations, one or more trust metrics are determined based on at least one of the following: media content, metadata corresponding to the media content, and trust records corresponding to the media content.

[0050] In possible implementations, one or more confidence metrics are encapsulated in a confidence certificate.

[0051] In possible implementations, the metrics include data indicating the reliability indicators that the media assets must meet.

[0052] In possible implementations, the processing module is: Determining whether a corresponding confidence metric within a metric is included in one or more confidence metrics, or determining whether one or more confidence metrics include a corresponding confidence metric within a metric. It is specifically configured to perform the following actions.

[0053] In possible implementations, the processing module is: To generate reliability information for media assets. Further configured to do so, the reliability information indicates whether one or more reliability metrics meet the metric, and the reliability metrics include parameters that indicate the reliability level of the media asset. In possible implementation forms, the processing module, Add reliability information to the trust record. It was further configured to perform the following actions.

[0054] In possible implementations, a trust record includes a trust manifest. The processing module is, Add reliability information to the reliability manifest. It is specifically configured to perform the following actions.

[0055] In possible implementations, the acquisition module is: Obtaining instruction information for media assets. It is further configured to do the following: The processing module is, Based on the instruction information, add reliability information to the reliability record corresponding to the media asset indicated by the instruction information. It is specifically configured to perform the following actions.

[0056] In possible implementations, reliability information is encapsulated in a reliability report.

[0057] In possible implementations, the processing module is: Generate files associated with media assets based on reliability information. It was further configured to perform the following actions.

[0058] In possible implementations, the first trust configuration information includes one of several trust configuration information, and the different trust configuration information indicates different domain or user metric requirements that the media asset must meet.

[0059] In possible implementations, the action of acquiring one or more confidence metrics for a media asset is triggered by capturing media content via hardware sensors or by generating media content via generation software.

[0060] In possible implementations, before acquiring multiple metrics and profiles of media assets, the acquisition module, Receiving requests for reliability assessment of media content It was further configured to perform the following actions.

[0061] In possible implementations, media content is at least one of the following: images, videos, or audio.

[0062] According to a fifth aspect, the present application provides a data processing device which includes: A capture module configured to retrieve media assets, wherein the media asset includes media content and at least one of metadata and trust records corresponding to the media content, and A processing module configured to determine one or more confidence metrics for a media asset, wherein the one or more confidence metrics are used to evaluate the reliability level of the media asset.

[0063] In possible implementations, one or more confidence metrics are encapsulated in a confidence certificate.

[0064] In possible implementations, one or more trust metrics are determined based on at least one of the following: media content, metadata corresponding to the media content, and trust records corresponding to the media content.

[0065] In possible implementations, one or more confidence metrics are used to determine the reliability information of a media asset, and this reliability information indicates whether one or more confidence metrics meet the metric.

[0066] In possible implementations, the metrics include data indicating the reliability indicators that the media assets must meet.

[0067] In possible implementations, the reliability metric includes parameters that indicate the reliability level of the media asset.

[0068] In possible implementations, media content is at least one of the following: images, videos, or audio.

[0069] According to a sixth aspect, one embodiment of the present application provides a data processing device, the device including: An acquisition module configured to acquire the first media content, and A processing module configured to generate a modification record for a first media content, wherein the trust record for the first media content includes initial information of the first media content, hard binding of the first media content, and a first digital signature, the first digital signature being a digital signature of first data, and the first data being data determined based on at least the initial information and hard binding of the first media content.

[0070] According to a seventh aspect, one embodiment of the present application provides a data processing system which includes: A first device configured to acquire media assets, A second device configured to acquire one or more confidence metrics of a media asset based on the media asset, wherein the first device and the second device may be the same device or different devices, and A third device configured to generate a trust report based on one or more trust metrics and trust configuration information of acquired media assets. In possible implementations, one or more trust metrics of the media assets are recorded in a trust certificate. In possible implementations, the second device sends one or more trust metrics of the media assets to the third device in response to a request from the third device. Trust configuration information may be recorded in a trust profile. In possible implementations, the third device may retrieve a trust profile from a trust profile repository. In possible implementations, the third device may directly read a trust profile from local memory.

[0071] According to the eighth aspect, one embodiment of the present application provides a data processing device which may include a memory, a processor, and a bus system, the memory being configured to store a program, and the processor being configured to execute the program in the memory to perform a method according to the first aspect and any optional implementation of the first aspect, a method according to the second aspect and any optional implementation of the second aspect, and a method according to the third aspect and any optional implementation of the third aspect.

[0072] According to the ninth aspect, one embodiment of the present application provides a computer-readable storage medium that stores a computer program, and when the computer program is executed on a computer, the computer can perform a method according to the first aspect and any optional implementation of the first aspect, a method according to the second aspect and any optional implementation of the second aspect, and a method according to the third aspect and any optional implementation of the third aspect.

[0073] According to the tenth aspect, one embodiment of the present application provides a computer program product including instructions, and when the instructions are executed on a computer, the computer is able to perform the methods according to the first aspect and any optional implementation of the first aspect, the second aspect and any optional implementation of the second aspect, and the third aspect and any optional implementation of the third aspect.

[0074] According to the eleventh aspect, the present application provides a chip system comprising a processor configured to support a data processing device in order to implement some or all of the functions in the preceding aspects, for example, transmitting or processing data or information in the manner described above. In possible designs, the chip system further comprises memory configured to store program instructions and data required for the data processing device. The chip system may comprise a chip, or a chip and other separate components. [Brief explanation of the drawing]

[0075] [Figure 1(1)] The application scenarios are shown below. [Figure 1(2)] The application scenarios are shown below. [Figure 2] This shows the structure of the terminal device. [Figure 3] This shows the server structure. [Figure 4] This indicates a cloud service. [Figure 5] This shows the application architecture. [Figure 6A] This shows a framework for trust records. [Figure 6B] The procedure for data processing is shown below. [Figure 7] This demonstrates the generation of reliability information. [Figure 8] This demonstrates the generation of reliability information. [Figure 9] The procedure for data processing is shown below. [Figure 10] The procedure for data processing is shown below. [Figure 11A] The procedure for data processing is shown below. [Figure 11B] The procedure for data processing is shown below. [Figure 12A] The procedure for data processing is shown below. [Figure 12B] The procedure for data processing is shown below. [Figure 13] The procedure for data processing is shown below. [Figure 14]The procedure for data processing is shown below. [Figure 15] The procedure for data processing is shown below. [Figure 16A-1] The procedure for data processing is shown below. [Figure 16A-2] The procedure for data processing is shown below. [Figure 16B] The procedure for data processing is shown below. [Figure 16C] The procedure for data processing is shown below. [Figure 17] This shows the structure of the data processing device. [Figure 18] The structure of the data processing device is shown. [Figure 19] This shows the server structure. [Modes for carrying out the invention]

[0076] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings of embodiments of the present invention. The terms used in the embodiments of the present invention are intended solely to describe specific embodiments of the present invention and are not intended to limit the present invention.

[0077] Embodiments of this application will be described below with reference to the accompanying drawings. Those skilled in the art will understand that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0078] In the specification, claims, and accompanying drawings of this application, terms such as “first,” “second,” etc., are intended to distinguish similar subjects and do not necessarily indicate a specific order or sequence. It should be understood that such terms are interchangeable in appropriate contexts and are merely distinguishable to describe objects having the same attributes in the embodiments of this application. In addition, the terms “include,” “have,” and any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, system, product, or device comprising a set of units is not necessarily limited to those units and may include other units not explicitly enumerated or specific to such process, method, product, or device.

[0079] As used herein, “substantially,” “about,” and similar expressions are intended to be approximate terms, not terms of degree, and are intended to take into account the inherent deviations of measured or calculated values ​​known to those skilled in the art. In addition, “may” as used to describe embodiments of the present invention means “one or more possible embodiments.” As used herein, the terms “use,” “using,” and “used” may be considered synonymous with the terms “utilize,” “utilizing,” and “utilized,” respectively. In addition, the term “example” is intended to refer to an example or illustration.

[0080] First, let's describe the application scenarios for this application.

[0081] This application may provide users with reliability information (sometimes referred to as a reliability report) that can be used as a basis for determining the reliability level of media assets.

[0082] In a particular scenario, embodiments of this application may be applied to an application that provides reliability information for media assets.

[0083] In one scenario, embodiments of this application may be applied to a cloud service that provides reliability information for media assets.

[0084] In a particular scenario, embodiments of the present application may be applied to a media asset management module. The module may generate reliability information for media assets, use the reliability information to update reliability records within the media assets, or generate files associated with the media assets based on the reliability information, which may help a user determine the reliability level of the media assets.

[0085] This application may provide users with a confidence index (sometimes called a confidence certificate) that can be used as a basis for determining the confidence level of media assets.

[0086] In a particular scenario, embodiments of this application may be applied to an application that provides a certificate of trust.

[0087] In one scenario, embodiments of this application may be applied to a cloud service that provides a certificate of trust.

[0088] In one scenario, embodiments of the present application may be applied to a media asset management module. The module may generate trust certificates for media assets, use the trust certificates to update trust records within the media assets, or generate files associated with the media assets based on the trust certificates, which may help a user determine the trust level of the media assets.

[0089] The aforementioned application scenarios will be explained separately below.

[0090] Scenario 1: Application

[0091] In one scenario, the embodiments of this application take the form of an application for providing reliability information or a certificate of reliability for a media asset (which in the embodiments of this application may be abbreviated as a reliability determination application).

[0092] The product form of the embodiment of this application may be a reliability determination application. The reliability determination application may run on a terminal device or a cloud-side server.

[0093] In possible implementations, a reliability determination application may, in response to input of media content or instructional information for a media asset, present the user with reliability information or a certificate of reliability for that media asset.

[0094] Figures 1(1) and 1(2) illustrate an example of an application scenario.

[0095] When a user needs to determine the reliability level of media content, they may launch a client of the media reliability platform on their terminal device (e.g., an application, applet, or web page) and perform a query.

[0096] For example, if user A's mobile phone A receives media content sent by user B's mobile phone B, and user A needs to use (for example, forward) the media content, user A may first determine the reliability level of the media content. After determining the reliability level of the media content, user A decides whether or not to use the media content.

[0097] It should be understood that mobile phone A may, alternatively, be another electronic device with strong computing power, such as a personal computer, computer workstation, or tablet computer. This is not limited to the present application.

[0098] For example, if a user needs to determine the reliability level of media content on a smartwatch, the user may send the media content on the smartwatch to their mobile phone, which then determines the reliability level of the media content and sends the reliability information to the smartwatch.

[0099] It should be understood that a smartwatch may, alternatively, be another electronic device with weaker computing power, such as a media consumption device, wearable device, set-top box, or game console. This is not limited to this application. A mobile phone may, alternatively, be another electronic device with stronger computing power, such as a personal computer, computer workstation, or tablet computer. This is not limited to this application.

[0100] Referring to Figure 1(1), the main interface 101 of the media reliability platform may include one or more controls, including, but not limited to, input boxes, reliability level determination buttons, etc. This is not limited to the present application.

[0101] For example, the user may tap an input box, and the media reliability platform may display a file selection interface in response to the user's actions. Then, after selecting the corresponding media content on the file selection interface, the user may tap the reliability information generation button on the main interface 101. Correspondingly, the media reliability platform may obtain reliability information for the media asset in response to the user's actions.

[0102] For example, a media reliability platform may display reliability information. For example, in this embodiment of the present application, the reliability information may include whether at least one reliability metric, which is of a media asset and is for media content, meets metric requirements in a predefined profile.

[0103] Referring to Figure 1(2), the verification results displayed in Figure 1(2) include whether the media content's confidence metrics T1, T3, T4, and Tn meet the metric requirements defined within the predefined profile. For example, confidence metric T1 meets the metric requirements, confidence metric T2 meets the metric requirements, confidence metric T3 does not meet the metric requirements, and confidence metric T4 meets the metric requirements.

[0104] In possible forms, a client of a media reliability platform may identify reliability information for media assets. For example, if there is one media asset to be identified, a client of a media reliability platform may perform the identification locally.

[0105] In possible forms, the media reliability platform server can identify reliability information for media assets. For example, if there are multiple media assets to be identified, a media reliability platform client may send multiple media assets to be identified to the media reliability platform server, which will identify the reliability information for the media assets and return the identified reliability information to the media reliability platform client.

[0106] It should be noted that in this application, whether a client of the media reliability platform identifies the reliability information of media assets, or whether a server of the media reliability platform identifies the reliability information of media assets, is not limited by the number of media contents to be identified.

[0107] The scenario descriptions corresponding to Figures 1(1) and 1(2) are also applicable to the generation of trust certificates, and it should be understood that similarities will not be explained again in this specification.

[0108] The following describes the product configuration that terminal 100 takes when the media reliability platform is located on the terminal side.

[0109] The terminal 100 in the embodiments of this application may be a mobile phone, tablet computer, wearable device, in-vehicle device, augmented reality (AR) / virtual reality (VR) device, notebook computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), etc. This is not limited to the embodiments of this application.

[0110] Figure 2 shows an optional hardware structure for terminal 100.

[0111] As shown in Figure 2, the terminal 100 may include components such as a radio frequency unit 110, memory 120, input unit 130, display unit 140, camera 150 (optional), audio circuit 160 (optional), speaker 161 (optional), microphone 162 (optional), processor 170, external interface 180, and power supply 190. Those skilled in the art will see Figure 2 It should be understood that this is merely an example of a terminal or multifunction device and does not constitute a limitation on terminals or multifunction devices. A terminal or multifunction device may contain more or fewer components than those shown in the figure, or may contain a combination of some components, or may contain different components.

[0112] The input unit 130 receives the input digital or character information. Terminal 100It can be configured to generate button signal inputs related to user settings and function control. Specifically, the input unit 130 may include a touchscreen 131 (optional) and / or another input device 132. The touchscreen 131 can collect touch operations performed by the user on or near the touchscreen 131 (e.g., operations performed by the user on or near the touchscreen using any suitable object such as a finger, knuckle, or stylus) and drive corresponding connected devices based on a pre-configured program. The touchscreen may detect touch operations performed by the user on the touchscreen, convert the touch operations into touch signals, and send the touch signals to the processor 170, which can then receive and execute commands sent by the processor 170. The touch signals include at least touch point coordinate information. The touchscreen 131 can provide input and output interfaces between the terminal 100 and the user. In addition, the touchscreen can be implemented in several types, such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touchscreen 131, the input unit 130 may include another input device. Specifically, another input device 132 is a physical keyboard, function buttons (volume control buttons) Nma The on / off button Nna This may include, but is not limited to, one or more of the following: trackballs, mice, joysticks, etc.

[0113] The input device 132 can receive a media asset specified for reliability level determination.

[0114] The display unit 140 may be configured to display information entered by the user or provided to the user, various menus of the terminal 100, interaction interfaces, files, and / or play any multimedia files. In the embodiments of this application, the display unit 140 may be configured to display reliability information (e.g., a reliability report), etc.

[0115] Memory 120 may be configured to store instructions and data. Memory 120 may primarily include an instruction storage area and a data storage area. The data storage area may store various types of data, such as multimedia files and text. The instruction storage area may store software units, such as instructions required for an operating system, an application, and at least one function, a subset thereof, or an extended set thereof. Memory 120 may further include non-volatile random access memory and provide support to the processor 170 in managing hardware, software, and data resources within the computing device to support control over software and applications. Memory 120 may be further configured to store multimedia files, run programs, and store applications.

[0116] The processor 170 is the control center of the terminal 100, connecting all parts of the terminal 100 via various interfaces and lines, executing various functions of the terminal 100, executing or implementing instructions stored in memory 120, and processing data by retrieving data stored in memory 120, thereby performing overall control over the terminal device. Optionally, the processor 170 may include one or more processing units. for exampleThe application processor and modem processor may be integrated into the processor 170. The application processor primarily handles the operating system, user interface, applications, etc. The modem processor primarily handles wireless communications. It may be understood that the modem processor does not have to be integrated into the processor 170. In some embodiments, the processor and memory may be implemented on a single chip. In some embodiments, the processor and memory may, alternatively, be implemented on separate chips. The processor 170 may be further configured to generate corresponding operation control signals, transmit operation control signals to corresponding components in the computing processing device, read and process data in the software, particularly data and programs in memory 120, to perform functions corresponding to functional modules in the processor 170, and thus control corresponding components to perform operations required by instructions.

[0117] Memory 120 may be configured to store software code related to the data processing method. The processor 170 may execute steps of the chip's data processing method, or it may schedule other units (e.g., input unit 130 and display unit 140) to implement corresponding functions.

[0118] The radio frequency unit 110 (optional) may be configured to receive and transmit signals while receiving and transmitting information or during a call. For example, after receiving downlink information from a base station, the radio frequency unit 110 transmits the downlink information to a processor 170 for processing. In addition, the radio frequency unit 110 transmits relevant uplink data to the base station. Typically, the RF circuit includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier (LNA), a duplexer, etc. Furthermore, the radio frequency unit 110 may communicate with network equipment and other devices via wireless communication. Wireless communications may use any communication standard or protocol, including but not limited to Global System for Mobile communications (GSM®), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA®), Long Term Evolution (LTE), email, and Short Messaging Service (SMS).

[0119] In the embodiments of this application, the radio frequency unit 110 can transmit media content to the server 200 and receive reliability information transmitted by the server 200.

[0120] Please understand that the radio frequency unit 110 is optional and may be replaced with another communication interface, such as a network interface.

[0121] Terminal 100 further includes a power supply 190 (e.g., a battery) that supplies power to each component. for example The power supply is logically connected to the processor 170 via a power management system, and can perform functions such as charge management, discharge management, and power consumption management via the power management system.

[0122] Terminal 100 further includes an external interface 180. The external interface may be a standard micro USB interface or a multi-pin connector, and may be configured to connect terminal 100 to another device for communication, or to connect to a charger for charging terminal 100.

[0123] Although not shown in the illustration, terminal 100 may further include a flash, a wireless fidelity (Wi-Fi) module, a Bluetooth® module, sensors with different functions, and the like. Further details are not described herein. Some or all of the methods described below may be applied to terminal 100 shown in Figure 2.

[0124] The following describes the product configuration that Server 200 takes when the media reliability platform is located on the server side.

[0125] Figure 3 is a diagram of the structure of server 200. As shown in Figure 3, server 200 includes a bus 201, a processor 202, a communication interface 203, and memory 204. The processor 202, memory 204, and communication interface 203 communicate with each other via bus 201.

[0126] Bus 201 may be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, or other similar buses. Buses can be classified into address buses, data buses, control buses, etc. For ease of representation, only one thick line is used to represent buses in Figure 3, but this does not mean that only one type of bus exists.

[0127] The processor 202 may be any one or more of the following processors: a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0128] Memory 204 may include volatile memory, such as random access memory (RAM). Memory 204 may also include non-volatile memory, such as read-only memory (ROM), flash memory, and hard disk drives. disk This may further include a hard drive (HDD) or a solid-state drive (SSD).

[0129] Memory 204 may be configured to store software code related to the data processing method. Processor 202 may execute steps of the chip's data processing method, or it may schedule another unit to implement the corresponding function.

[0130] It should be understood that terminal 100 and server 200 may be centralized or distributed devices. The processors within terminal 100 and server 200 (e.g., processor 170 and processor 202) may be hardware circuits (e.g., application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), general-purpose processors, digital signal processors (DSPs), microprocessors, or microcontrollers), or combinations thereof. For example, a processor may be a hardware system with instruction execution capabilities, such as a CPU or DSP, a hardware system without instruction execution capabilities, such as an ASIC or FPGA, or a combination of a hardware system without instruction execution capabilities and a hardware system with instruction execution capabilities.

[0131] Scenario 2: Cloud Services

[0132] In one scenario, the embodiments of this application take the form of a cloud service for providing reliability information or a certificate of reliability for media assets.

[0133] In possible implementations, the server may provide a service to the client side via an application programming interface (API) to query media asset reliability information or trust certificates.

[0134] end In the end Relevant parameters (e.g., data such as media assets) can be sent to the server via an API provided by the cloud. The server can then retrieve processing results (e.g., reliability information or a certificate of trust) based on the received parameters and return the results to the terminal.

[0135] For a description of the terminals and servers, please refer to the description in the embodiments described above. Further details will not be provided in this specification.

[0136] Figure 4 shows the procedure for using a cloud service with reliability level determination capabilities provided by a cloud platform.

[0137] 1. Launch and purchase the content auditing service.

[0138] 2. Users may download a software development kit (SDK) that supports the content auditing service. Typically, cloud platforms provide multiple development versions of the SDK for users to choose from based on their development environment requirements, such as a Java® version, a Python version, a PHP version, and an Android version.

[0139] 3. The user downloads the corresponding version of the SDK to their local computer as needed, imports the SDK project into the local development environment, configures and debugs the SDK project in the local development environment, and the local development environment also supports the development of other functions. As a result, an application integrating the reliability level determination function is formed.

[0140] 4. When using a reliability level determination application, an API call for the reliability level determination function may be triggered when it is necessary to perform the reliability level determination function. When the application triggers the reliability level determination function, an API request is initiated to a running instance of the reliability level determination service in the cloud environment, and the API request carries media content. The running instance in the cloud environment processes the media content to obtain reliability information.

[0141] 5. The cloud environment returns reliability information to the application, thereby completing a single call to the reliability level determination function.

[0142] Scenario 3: Media Asset Management Module

[0143] In this scenario, the terminal device may capture or generate media content. For example, the terminal device may take an image via a camera built into the terminal device, or it may generate new media content via media content editing software (e.g., AIGC).

[0144] When acquiring media content, the terminal device must further generate the corresponding media asset via a media asset management module, and the media asset may include metadata, trust records, etc.

[0145] In this scenario, the media asset management module can generate trustworthiness information based on the content within the media asset and either add the trustworthiness information to the media asset or use the trustworthiness information as a file associated with the media asset.

[0146] Figure 5 is a block diagram of the structure of terminal device 101 where the media asset management module is located.

[0147] According to one or more implementations described in this application, the terminal device 101 may include electronic components for managing media items (e.g., media assets). terminalDevice 101 can be housed in a single computing system (such as a desktop computer system, laptop computer system, tablet computer system, server computer system, mobile phone, media player, personal digital assistant, personal communicator, game device, network router, network hub, wireless access point (AP), repeater, set-top box, or a combination thereof). Each component of terminal device 101 can be implemented on separate computing systems that are spatially isolated and connected via communication technology 110.

[0148] In one implementation, terminal One or more components within device 101 may be implemented as one or more integrated circuits (ICs). For example, at least one of the processing unit 104, media content capture device 102, peripheral device 118, sensor 122, or memory 110 may be implemented as a system-on-chip (SoC) IC, a 3D IC, any other known IC, or any combination of known ICs. In another implementation form, terminal Two or more components within device 101 are implemented together as one or more ICs. For example, at least two of the processing unit 104, media content capture device 102, peripheral device 118, sensor 122, or memory 110 may be implemented together as a SoC IC. In the following, terminal Each component of device 101 will be described.

[0149] The terminal device 101 may include a media content capture device 102 (for example, an imaging device for capturing images, an audio device for capturing sound, a multimedia device for capturing audio and video, or any other known user media content capture device). In one implementation, the media content capture device 102 may further include a signal processing pipeline implemented as hardware, software, or a combination thereof. The signal processing pipeline may perform one or more operations on data received from one or more components within the device 102. The signal processing pipeline may also provide the processed data to memory 110, peripheral devices 118 (described further below), and / or processing units 104.

[0150] The terminal device 101 may include a processing unit 104 such as a CPU, GPU, another integrated circuit (IC), memory, and / or other electronic circuitry. In one implementation, the processing unit 104 may generate media assets 111 associated with media content 114 through a media asset management module 106. In some implementations, the processing unit 104 may generate reliability information based on the content within the media assets and add the reliability information to the media assets, or use the reliability information as a file associated with the media assets (e.g., a reliability report 121 shown in Figure 5).

[0151] The terminal device 101 may further include a peripheral device 118. In one implementation, the peripheral device 118 is (i) terminal (ii) One or more input devices (e.g., mouse, keyboard, etc.) that interact with one or more components of device 101 or transmit data terminal(iii) may include at least one of the following: one or more output devices that provide output from one or more components of device 101 (e.g., monitors, printers, and display devices), or one or more storage devices that store data other than memory 110. Peripheral device 118 is terminal The peripheral device 118 is shown in a dashed box to indicate that it is an optional component of device 101. Alternatively, the peripheral device 118 may be a single component or device (e.g., a touchscreen) that can be used as both an input and an output device. terminal Device 101 may include at least one peripheral control circuit (not shown) for a peripheral device 118. The peripheral control circuit may be a controller (e.g., a chip, an expansion card, or a separate device). The controller interacts with the peripheral device 118 and is configured to indicate the operation to be performed by the peripheral device. The peripheral device controller may be a separate processing unit or may be integrated into processing unit 104. The peripheral device 118 may also be referred to as an input / output (IO) device 118 throughout this specification.

[0152] The terminal device 101 may further include one or more sensors 122. One or more sensors are sensors terminal The dashed box indicates that it may be an optional component of device 101. In one implementation, sensor 122 may detect one or more characteristics of the environment. Examples of sensors include, but are not limited to, optical sensors, image sensors, accelerometers, sound sensors, barometric pressure sensors, proximity sensors, vibration sensors, gyroscope sensors, compasses, barometers, thermal sensors, rotation sensors, speed sensors, and inclinometers.

[0153] Referring to Figures 4 and 10, this embodiment provides a reliability evaluation system, which includes: A first device configured to acquire media assets, A second device configured to acquire one or more confidence metrics of a media asset based on the media asset, wherein the first device and the second device may be the same device or different devices, and A third device configured to generate a trust report based on one or more trust metrics and trust configuration information of acquired media assets. In possible implementations, one or more trust metrics of the media assets are recorded in a trust certificate. In possible implementations, the second device sends one or more trust metrics of the media assets to the third device in response to a request from the third device. Trust configuration information may be recorded in a trust profile. In possible implementations, the third device may retrieve a trust profile from a trust profile repository. In possible implementations, the third device may directly read a trust profile from local memory.

[0154] Figure 6A shows the framework for trust records of media assets.

[0155] For example, as shown in Figure 7, a media asset may include media content, metadata for the media content, and trust records for the media content. The metadata and trust records for the media content are either bound to the media content, or they are associated with the media content.

[0156] When media assets of media content are retrieved, the metadata and trust records of the media content are also retrieved. For example, when an image is retrieved, the image in digital format, as well as the image's metadata and trust records, may be retrieved.

[0157] Note that in some scenarios, media assets are also referred to as media files or digital assets.

[0158] Media content may be images, audio, video, or text.

[0159] A media asset may include a trust record. In one implementation, the trust record may be part of the metadata. In one implementation, the trust record and metadata may exist independently of each other within the media asset. For example, media content is sometimes called digital content. Media content may be part of a media asset and represents the actual content of the media. Digital content is content of different content types that exist in digital format, such as text, images, and audio, and can be stored on digital carriers such as optical discs and hard disks and transmitted via means such as networks. Digital content is a general term for products or services that integrate and use content such as images, text, audio, and video through digital technology, and is a product that combines digital media technology with cultural creativity. For example, media content may be the pixel data of an image and any additional technical metadata necessary to understand or present the content (e.g., color profiles or encoding parameters).

[0160] Digital technology is a science and technology developed alongside electronic computers, which uses specific devices to convert various types of information, including images, text, audio, and video, into binary digits "0" and "1" that can be identified by electronic computers for computation, processing, storage, transmission, distribution, and recovery. Because processes such as computation and storage require the use of computers for information encoding, compression, and decoding, digital technology is also called digital tech or computer digital technology. Digital technology is also called digital control technology.

[0161] For example, metadata may include data used to describe media content, and metadata may record non-technical information about media assets or media content. For example, metadata may include information describing the properties of media content. For example, metadata may be a trust document (or trust manifest). For example, metadata may include the location, creator, annotations, or IPR information of media content.

[0162] For example, as shown in Figure 8, a trust record for media content may be a type of metadata for the media content (or it may not belong to metadata, but be independent of metadata and belong to the media asset). Trust records can be used to record relevant information about the creation and transition of media content and to track the origin of media content.

[0163] In one embodiment, as shown in Figure 6A, the trust record may include a trust declaration and a trust manifest.

[0164] For example, a trust declaration may be a type of trust manifesto and may occupy the first position in a trust record. Alternatively, a trust declaration may be a type of metadata for media content.

[0165] In possible implementations, a Trust declaration within a Trust record contains initial information about the first media content, hard bindings for the first media content, and a first digital signature. sk0 This may include, where the first digital signature is the digital signature of the first data, and the first data is data determined based on at least the initial information and hard binding of the first media content.

[0166] The first media content may be different types of content that exist in digital format, such as text, images, and audio. For example, the first media content may be the pixel data of an image and any additional technical metadata necessary to understand or present the content (e.g., a color profile or encoding parameters).

[0167] Hardbinding refers to data obtained by performing a hardbinding process on the original data. Hardbinding is a processing method used to prevent the original data from being forged, and the original data cannot be obtained by performing a reverse process on the hardbinding of the original data. The hardbinding of the original data is obtained by performing a hardbinding process on the original data. For example, the original data is processed using a one-way function. For example, hashing may be a type of hardbinding process. In other words, hardbinding may include a hash value. The hardbinding of the first media content may include the first media hash value, where the first media hash value (Media Hash 0) is the hash value of the first media content.

[0168] For example, the first data is data obtained by combining at least the hardbinding of the first media content with the initial information of the first media content, or the first data is data obtained by combining at least the hardbinding of the initial information (e.g., the hash of the AI-generated AIGC, AIGC.Hash, or the hash value of the media generation method, Media Type.Hash) with the hardbinding of the first media hardbinding (e.g., the hash value of the first media hash value, Media Hash 0.Hash).

[0169] For example, the first data may alternatively be a first combined hash value (Hash(TD)), the first combined hash value including a hash value obtained by combining initial information of the first media content and the first media hash value. For example, the first combined hash value including a hash value obtained by combining initial information of the first media content, the first media hash value, and other descriptive information, the other descriptive information not having to be other descriptive information within the initial information, but for example, information about a tool for generating a trust profile, or other information describing the initial information of the first media content. This is not limited to the present application.

[0170] For example, the first data may alternatively be the first combined hash value, and the first combined hash value may include a hash value obtained by combining the hash value of the initial information of the first media content and the hash value of the first media hash value (Media Hash 0. Hash). For example, the first combined hash value is a hash value obtained by combining the hash value of the initial information of the first media content, the hash value of the first media hash value, and the hash values ​​of other metadata information. Here, if there are at least two pieces of initial information for the first media content, the hash value of the initial information of the first media content may include the hash value of each piece of initial information for the first media content, or the hash value of the initial information of the first media content may include a hash value obtained by combining all of the initial information for the first media content.

[0171] For example, initial information of media content may be information generated when the media content is produced. For example, initial information of media content may include at least one of the following: the time of production of the media content, the name of the creator of the media content, the digital content identifier of the media content, the production location of the media content, information about the device that produced the media content, the resolution of the media content, the size of the media content, the media type of the media content, or the method of production of the media content. For example, information about the device that produced the media content may include the model of the device, camera parameters, etc., and camera parameters may include the focal length, sensitivity, exposure, etc., of the camera during photography. It should be understood that initial information of media content may further include other information, such as copyright information, which is used to describe the media content. This is not limited to this application.

[0172] For example, media types may include, but are not limited to, image types, video types, audio types, graphics types, etc. Generation methods may include AI generation methods, non-AI generation methods, etc. In possible forms, the generation method in the initial information may be replaced with an AI generation (AIGC) identifier. For example, if the media content is generated by AI, the value of the AIGC identifier is 1, and if the media content is not generated by AI, the value of the AIGC identifier ID is 0. The creator name of the media content may be the name of the creator of the media content or the name of the device that captured the media content. This is not limited in this application.

[0173] The first data is digitally signed, typically by using a private key to obtain a digital signature of the first data.

[0174] For example, the private key may be the private key of an electronic device or a user. For example, first data may be digitally signed by using a digital signature algorithm and a private key to obtain a digital signature of the first data. It should be understood that the digital signature algorithm is not limited in this application.

[0175] For example, a trust manifest records information indicating the origin of a media asset. A trust manifest is part of a trust record. Alternatively, a trust manifest may be a type of metadata.

[0176] For example, a trust manifest may include a hardbinding of the (N+1)th media content (e.g., a second media hash value, Media Hash 1). For example, media content obtained by performing an edit operation on the Nth media content based on an edit operation, or media content obtained by modifying the metadata of the Nth media content, or the Nth media content that should be transferred (or shared) may be called the (N+1)th media content.

[0177] For example, a trust manifesto may further include media assertions, which may include copyright information, thumbnails, whether modifications are permitted, permission to operate, permission to use, and the source of secondary media content.

[0178] For example, a trust manifesto is a second digital signature. sk1 It may further include a second digital signature which is a digital signature of the second data which may be data determined based on at least the second media hash value and the modification assertion.

[0179] For example, the second data is data obtained by combining at least the (N+1)th media hash value and the (N+1)th assertion, or the 2 The data is obtained by combining the hash value of at least the (N+1)th assertion and the hash value of the (N+1)th media hash value.

[0180] For example, the second data could alternatively be the (N+1)th combined hash value, which is the (N+1)th assertion and the (N+1)th media hash. Value and This includes hash values ​​obtained by combining the (N+1) assertions. For example, the (N+1) combined hash value is obtained by combining the (N+1) assertions. 、 The (N+1)th media hash value and other descriptive information The hash obtained by combining the two Value The included and other descriptive information does not have to be other descriptive information within the (N+1)th assertion, but may be, for example, information about a tool for generating a trust manifest, or other information describing the (N+1)th assertion. This is not limited to the present application.

[0181] For example, the second data could alternatively be the (N+1)th combined hash value, which is obtained by combining the hash value of the (N+1)th assertion and the hash value of the (N+1)th media hash value (e.g., the second media hash value) hash value Media Hash 1. Hash) may be included. For example, the (N+1)th combined hash value is a hash value obtained by combining the hash value of the (N+1)th assertion, the hash value of the (N+1)th media hash value, and the hash value of other metadata information (Other Information).

[0182] For example, the trust manifest may further include a first Uniform Resource Identifier (URI), the first URI indicating the location of the initial information or hardbinding of the first media content in the modification record of the first media content. For example, URI1 indicates the location of the hardbinding of the first media content in the trust record of the first media content, URI2 indicates the location of the media type in the trust record of the first media content, URI3 indicates the location of the AIGC in the trust record of the first media content, and URI4 indicates the (N+1)th media content in the trust record (e.g., the second media hash value Media Hash 1). hard binding URI5 indicates the location of the media assertion in the trust record.

[0183] For example, the modification record may contain other information, which may not be used to verify the authenticity of the media content, such as the file name and the name of the hash algorithm. The hash algorithm is used to calculate the hash value mentioned in the embodiments described above.

[0184] The following describes the method procedure in the embodiment of this application using an example.

[0185] Figure 6B shows the procedure of a data processing method according to one embodiment of the present application. This procedure mainly describes the process of generating reliability information. As shown in Figure 6B, the data processing method provided in this embodiment of the present application includes the following steps.

[0186] 601: Obtain one or more confidence metrics for a media asset, where the media asset includes media content, metadata corresponding to the media content, and at least one confidence record corresponding to the media content.

[0187] For example, a trust indicator is a parameter that indicates the reliability level of a media asset. In possible implementations, the trust indicator of a media asset may be obtained from a trust certificate, i.e., the trust indicator of a media asset is encapsulated in a trust certificate. In possible implementations, the trust indicator may be obtained from the media asset, and the corresponding trust indicator is read directly from the media asset. In possible implementations, a media asset includes at least one of media content, metadata corresponding to the media content, and a trust record corresponding to the media content. In this embodiment of the application, one or more trust indicators related to the reliability level of a media asset may be determined based on the media asset, in order to enable a user to determine the reliability level of the media asset. The user can analyze and determine the reliability level of the media asset based on the verification results of the trust indicators.

[0188] In possible implementations, as shown in Figure 8, a media asset may include media content and metadata for the media content. The metadata for the media content may be bound to the media content, or the metadata for the media content may be associated with the media content. The metadata may include trust records, or it may not include trust records, and the trust records and metadata may be different parts of the media asset. In other words, as shown in Figure 8, a media asset may include media content and metadata corresponding to the media content (with trust records encapsulated in the metadata), or as shown in Figure 7, a media asset may include media content, metadata corresponding to the media content, and trust records corresponding to the media content.

[0189] In possible implementations, a trust record may be further divided into a trust declaration and several trust manifests. A trust manifest is a set of media asset source information. A trust declaration is a special type of trust manifest that contains only mandatory assertions (mandatory source information). A trust declaration may appear in the first position of a trust record.

[0190] In possible implementations, one or more trust metrics related to the reliability level of a media asset may be determined based on the media asset. Specifically, as shown in Figure 7, one or more trust metrics related to the reliability level of a media asset may be determined based on at least one of the media content, metadata corresponding to the media content, and a trust record corresponding to the media content. As shown in Figure 8, one or more trust metrics related to the reliability level of a media asset may be determined based on at least one of the media content and metadata corresponding to the media content (the trust record is part of the metadata).

[0191] For example, the trust metric for a media asset may be features extracted from media content, metadata corresponding to media content, and trust records corresponding to media content, or it may be data obtained by performing specific processing on features within media content, metadata corresponding to media content, and trust records corresponding to media content.

[0192] For example, one or more reliability indicators TM related to the reliability level of a media asset may be determined based on the metadata of the media content. For example, TM may be the time the media content was created, the name of the creator of the media content, the digital content identifier of the media content, the location where the media content was created, information about the device used to create the media content, the media type of the media content, or the method of creating the media content. For example, information about the device used to create the media content may include the model of the device, camera parameters, etc., and camera parameters may include the focal length, sensitivity, exposure, etc., of the camera during photography. It should be understood that TM may further include other information, such as copyright information, which is not limited to this application.

[0193] For example, one or more confidence metrics related to the reliability level of a media asset may be determined based on the metadata of the media content and the confidence record of the media content.

[0194] For example, one or more trust metrics related to the trustworthiness level of a media asset may be determined based on the metadata of the media content, the media content itself, and the trustworthiness record of the media content.

[0195] For example, one or more trust indicators (TRs) related to the reliability level of a media asset may be determined based on the media content's trust record. For example, the production time of the media content, the name of the media content's creator, the media content's digital content identifier, the media content's production location, information about the media content's production device, the media editing method, the media type of the media content, or the media content's production method may be selected as TRs within the trust record. For example, information about the media content's production device may include the device model, camera parameters, etc., and camera parameters may include the camera's focal length, sensitivity, exposure, etc., during photography. It should be understood that TRs may further include other information, such as copyright information, used to describe the media content. This is not limited to this application. The media production method may include, but is not limited to, whether the media content is produced via AIGC, whether the media content is produced via a camera, whether the media content is produced using software assistance, whether the media content is produced via synthetic media, and whether the media content can be used to train a model. The media editing method may include, but is not limited to, rotation, resizing, cropping, content editing, etc. The TR may further include media assertions, which may include thumbnails, whether modifications are permitted, permission to operate, permission to use, and the source of secondary media content.

[0196] For example, one or more confidence metrics related to the confidence level of a media asset may be determined based on the media content and the confidence record of the media content.

[0197] For example, one or more confidence metrics (TC) related to the reliability level of a media asset may be determined based on the media content. For instance, features such as people, scenes, object types, spatial relationships between objects, and media style (e.g., color, lighting, or brightness) within the media content may be selected as confidence metrics for the media content.

[0198] For example, one or more reliability metrics related to the reliability level of a media asset may be determined based on the metadata and media content of the media content.

[0199] A confidence metric can be a characteristic description of a media asset. For example, a confidence metric may include parameters that indicate the reliability level of a media asset.

[0200] In possible implementations, a trust certificate may include the relationship between each trust metric and its corresponding source; that is, the trust metric is a specific part of the media asset (metadata, trust). record It corresponds to (or media content). A trust credential is one or more trust metrics obtained based on a media asset.

[0201] In possible implementations, if a confidence metric is obtained, it can be used as a confidence certificate. In possible implementations, if multiple confidence metrics are obtained, all of them can be encapsulated to obtain a confidence certificate. In possible implementations, the confidence metrics and other data can be encapsulated together as an alternative to obtain a confidence certificate.

[0202] In possible implementations, for one or more acquired reliability metrics of a media asset, corresponding requirements, namely evaluation constraints on the reliability metrics of the media asset in a specific scenario, may be acquired. These evaluation constraints can be considered constraints related to the reliability level of the media asset.

[0203] In this embodiment of the present application, evaluation constraints on the reliability metrics of a media asset may be described by using a profile, which may include metric requirements corresponding to each reliability metric. The profile may also be called a trust profile. The trust profile records a defined set of reliability metrics, which can be used to evaluate the reliability certificate of a particular media asset.

[0204] 602: Determine whether one or more confidence metrics meet the metric.

[0205] In one implementation, metrics can be stored in memory. When it is necessary to evaluate the reliability of a media asset, one or more reliability metrics of the media asset are retrieved, and a matching is performed between the reliability metrics corresponding to the metric and one or more reliability metrics of the media asset to determine whether one or more reliability metrics satisfy the metric.

[0206] In one implementation, each metric defined in the trust profile is specific to the trust indicator. Based on the metrics defined in the profile, the corresponding trust indicator may be obtained from the media asset, or the reliability information of the media asset may be obtained based on the trust profile and one or more trust indicators of the media asset.

[0207] For example, a trust profile records trust configuration information, which includes metrics that a media asset must meet. In other words, trust configuration information includes metrics that a trust certificate must meet, and trust configuration information can be a set of trust metrics used to evaluate a trust certificate and indicate the trust level of a media asset.

[0208] In one implementation, multiple metrics for a media asset may be acquired initially, and these multiple metrics may include metrics other than the constraint metrics in the profile. Therefore, in order to obtain reliability information for a media asset, it is necessary to determine whether each reliability metric satisfies the metric. , This can be determined based on the metrics defined within the configuration file.

[0209] In one implementation, a metric may be data obtained through processing based on a confidence metric, but a metric may also indicate the corresponding confidence metric. That is, a metric may be a confidence metric of a media asset, a value of the confidence metric, or data obtained by performing a specific operation on the confidence metric.

[0210] In one implementation, a metric may include one or more confidence metrics, and it may be determined whether one or more confidence metrics of a media asset satisfy the metric by determining whether the confidence metrics within the metric are included in one or more confidence metrics of the media asset.

[0211] In one implementation, to determine whether one or more confidence metrics of a media asset satisfy a metric, it is determined whether one or more confidence metrics of the media asset contain confidence metrics within the metrics. In other words, when a confidence metric included in the confidence profile is included in the confidence metrics of a media asset, the confidence metrics of the media asset may be considered to satisfy the metric, or the confidence metric may be the content of the media asset's metadata, the content of the media asset's modification record, or a feature of the media content. As shown in Figure 7, the metrics in the confidence profile include TM1', TR1', TC1', and TM11', where TM1', TR1', TC1', and TM11' correspond to the confidence metrics TM1, TR1, TC1, and TM11. For example, TM1 may be Shenzhen, Guangdong Province, and TM1' may be a location within China. In this case, TM1 satisfies the metric. TM11 does not satisfy the corresponding metric because it does not exist in one or more confidence metrics of the media asset. In other words, a media asset does not meet the metric requirements of the trust configuration information if the trust metrics indicated by the metrics in the trust configuration information are not present in one or more of the media asset's trust metrics. A media asset's trust metrics may be considered to meet the metrics if the media asset's trust metrics include trust metrics that are included in the trust profile.

[0212] As shown in Figure 8, the metrics in the trust profile include TM1', TM2', TM3', and TC1', and the trust certificate contains the corresponding trust metrics TM1, TM2, TM3, and TC1. In other words, a media asset satisfies the metric requirements of the trust configuration information when all the trust metrics indicated by the metrics in the trust configuration information are present in one or more trust metrics of the media asset.

[0213] In one implementation, a metric may include a decision condition, which indicates whether a target confidence metric exists within one or more confidence metrics of a media asset. To determine whether one or more confidence metrics satisfy a metric, it may be determined whether one or more confidence metrics satisfy the decision condition. The confidence information may be presented as a confidence report. By using confidence profiles and confidence certificates as input, the results of evaluating confidence certificates based on confidence configuration information are generated and recorded. The evaluation is an assessment of the confidence level of a given media asset. The results may be provided to the user as confidence information.

[0214] Subsequently, the reliability information may be used to generate a new reliability manifest (details of which will be described in subsequent embodiments) or as a file associated with the media asset.

[0215] Me One or more confidence indicators of the Deer Asset Metrics in the profile Whether or not the requirements are met can be indicated by reliability information. Users can determine the reliability level of a media asset based on this reliability information.

[0216] The above outlines the basic steps of the method shown in Figure 6B. Below, we will describe, using examples, specific implementations that may be used for some of the steps of the method shown in Figure 6B.

[0217] In step 602, multiple profiles may be obtained because different domains or users may have different metric requirements for the same media asset. These multiple profiles may be stored in a profile repository, with each profile representing the metric requirements of one domain or user regarding the media asset. Therefore, there are several possible specific implementations for obtaining reliability information for media assets based on profiles and reliability metrics. The following provides an explanation with examples, referring to implementations A through D.

[0218] Implementation form A: The trust profile contains only one set of metrics.

[0219] For media asset reliability metrics, there may be only one set of metrics. In this case, whether the media asset's reliability metric satisfies the set of metrics can be calculated based on uniquely corresponding metric requirements to obtain reliability information.

[0220] Since there is only one set of metric requirements, the confidence index The target and corresponding metric requirements Please understand that the association relationships between them may not be recorded in the reliability information, meaning that the specific domain or user used by the metric requirements to obtain the reliability information may not be indicated.

[0221] Implementation form B: Multiple confidence profiles exist for a confidence metric, and corresponding confidence information is calculated for each confidence profile.

[0222] Because different domains or users have different requirements for reliability levels, each reliability profile includes a set of metrics, and different sets of metrics indicate the metric requirements of different domains or users that the media asset must meet. In this case, it may be possible to calculate whether the reliability metric meets multiple metric requirements and obtain multiple pieces of reliability information.

[0223] It should be understood that the association relationship between reliability metrics and corresponding metric requirements can be stored in reliability information so that the metrics indicate a specific area or specific user corresponding to the reliability information.

[0224] Implementation form C: Multiple trust profiles exist, and the corresponding trust information is calculated for only a portion of them (for example, one trust profile).

[0225] Different domains or users have different requirements regarding reliability levels, and multiple metric requirements may exist for the reliability metrics of media assets (different metric requirements within multiple requirements may correspond to different domains or different users). When the system calculates reliability information, the user may define how the calculation determines whether the reliability metrics of a media asset meet a particular set or multiple sets of metric requirements.

[0226] It should be understood that the association relationship between media asset reliability metrics and corresponding metric requirements can be stored in reliability information to indicate a specific area or specific user to which the metric requirement corresponds.

[0227] According to this application, in the aforementioned implementation, different scenarios (e.g., domain or user) may address different requirements, and multiple different reliability information may be generated, thereby satisfying the reliability level determination requirements for different scenarios.

[0228] Implementation form D: Multiple trust configurations may exist for a trust metric, and different trust configurations may indicate different domain or user metric requirements that the media asset must satisfy. Multiple trust configurations may be stored within one or more trust profiles. The reliability information of a media asset may be obtained based on a first configuration and the media asset's trust metric, where the first configuration is one of the multiple trust configurations.

[0229] After reliability information is obtained, media assets can be updated using that information. The following describes specific implementations of updating media assets using reliability information.

[0230] Figure 9 shows the procedure of a data processing method according to one embodiment of this application.

[0231] 901: Capture media content via hardware sensors or generate media content via generation software.

[0232] Terminal devices may capture media content via hardware sensors or generate media content via software. For example, a terminal device may take images via a camera built into the terminal device, or it may generate new media content via media content editing software (e.g., AIGC).

[0233] When acquiring media content, the terminal device must further generate the corresponding media asset via a media asset management module, and the media asset may include metadata, trust records, etc.

[0234] In this embodiment of the present application, the media asset management module may capture media content via hardware sensors or generate media content via generation software, generate reliability information based on the content within the media asset, and add the reliability information to the media asset.

[0235] It should be understood that step 901 can be understood as an action preceding step 902. For example, step 901 may be a trigger condition for step 902 (optionally, step 901 may be one of several trigger conditions for step 902).

[0236] 902: Obtain one or more confidence metrics for a media asset, where the media asset includes media content and at least one of the media content's metadata and media content's confidence record.

[0237] For a description of step 902, please refer to the description of step 601 in the previously described embodiment. Similarities will not be described again in this specification.

[0238] 903: To obtain reliability information, determine whether each reliability metric meets the criteria.

[0239] For a description of step 903, please refer to the description of step 602 in the previously described embodiment. Similarities will not be described again in this specification.

[0240] 904: Add reliability information to the trust record.

[0241] When adding reliability information to a reliability record, the reliability information can be added to an assertion.

[0242] Specifically, trustworthiness information can be signed and then added to an assertion.

[0243] For example, trustworthiness information corresponding to a single media content may be signed and then added to the assertion as one of the assertion items.

[0244] For example, the satisfaction status in the reliability information corresponding to each metric or some of the metrics may be signed and then added to the assertion as one of the assertion items.

[0245] By adding newly generated reliability information to the media asset's reliability record, the reliability of the media asset can be further improved.

[0246] In addition, the hash value of the reliability information can be further added to the claims within the reliability manifest.

[0247] In addition, signatures for reliability information may be added to the signatures in the reliability manifest.

[0248] Figure 6 BBased on this, Figure 10 shows the procedure of a data processing method according to one embodiment of the present application. Compared with Figure 6, the flowchart in Figure 10 illustrates the process of generating reliability information and updating media assets by using the relationships between functional modules. Specifically, the flowchart in Figure 10 includes: The system consists of a generation module, an extraction module, a trust profile storage module, and a verification module. The generation module is configured to generate media assets, each containing three parts: metadata, media content, and a trust record. The extraction module uses the media assets as input to extract trust information from the metadata, trust record, and media content of the media assets. index The system extracts trust metrics and generates trust certificates for media assets based on the extracted trust metrics. The trust profile storage module is responsible for storing trust profiles, which are predefined by entities such as governments, platforms, or users for specific scenarios. The validation module uses the trust certificates and trust profiles as input to evaluate whether the trust metrics in the trust certificates meet the predefined trust profiles. Finally, a trust report is generated based on the evaluation results.

[0249] Based on Figure 6, Figures 11A and 11B, and Figures 12A and 12B, respectively, illustrate the steps of a data processing method according to one embodiment of the present application. Compared with Figure 6, the flowcharts in Figures 11A and 11B, and Figures 12A and 12B, respectively, illustrate the processes of generating reliability information and updating media assets by using the relationships between functional modules. The difference between Figures 11A and 11B and Figures 12A and 12B is that in Figures 11A and 11B, the generation algorithm initiates the procedure, while in Figures 12A and 12B, the verification algorithm initiates the procedure.

[0250] Figures 11A and 11B specifically include the following steps.

[0251] 1: The generation algorithm is configured to generate media assets, and each media asset includes metadata, media content, and a trust record.

[0252] 2: The generation algorithm passes the media asset to the extraction algorithm.

[0253] 3: The extraction algorithm uses the media asset as input to extract a trust metric from the metadata, trust record, and media content of the media asset, and generates a trust certificate for the media asset based on the extracted trust metric.

[0254] 4: The extraction algorithm passes the trust certificate to the verification algorithm.

[0255] 5: The verification algorithm triggers the verification process.

[0256] 6: The verification algorithm requests to obtain a trust profile from the trust file storage side, and the trust profile is predefined by an entity such as the government, platform, or user for a specific scenario. Yorifu The trust file storage side passes the trust profile to the verification algorithm.

[0257] 7: The verification algorithm uses the trust certificate and the trust profile as input to evaluate whether the trust metric in the trust certificate meets the predefined trust profile, and generates a trust report based on the evaluation result.

[0258] 8: The verification algorithm generates a new trust manifest based on the trust report. <{

[0259] 9: The verification algorithm returns the trust manifest to the generation algorithm.

[0260] 10: The verification algorithm returns the trust manifest to the generation algorithm.

[0261] 11: The generation algorithm adds a new trust manifest to the trust record in order to update the media asset.

[0262] Figures 12A and 12B specifically include the following steps.

[0263] 0: The generation algorithm is configured to generate media assets, each media asset containing metadata, media content, and a trust record.

[0264] 1. The verification algorithm triggers the verification process.

[0265] 2: The verification algorithm requests that a trust profile be retrieved from the trust file storage side, and the trust profile is predefined by an entity such as a government, platform, or user for a specific scenario.

[0266] 3. The trusted file storage side passes the trust profile to the verification algorithm.

[0267] 4. The verification algorithm requests that the extraction algorithm obtain a certificate of trust.

[0268] 5. The extraction algorithm requests that the media assets be retrieved from the generation algorithm.

[0269] 6. The generation algorithm passes the media assets to the extraction algorithm.

[0270] 7. The extraction algorithm uses media assets as input to extract trust metrics from the media asset's metadata, trust records, and media content, and generates a trust certificate for the media asset based on the extracted trust metrics.

[0271] 8. The extraction algorithm passes the trust certificate to the verification algorithm.

[0272] 9: The verification algorithm uses the trust certificate and the trust profile as inputs to evaluate whether the trust metric in the trust certificate meets the pre-defined trust profile, and generates a trust report based on the result of the evaluation.

[0273] 10: The verification algorithm generates a new trust manifest based on the trust report.

[0274] 11: The verification algorithm returns the trust manifest to the generation algorithm.

[0275] 12: The generation algorithm adds the new trust manifest to the trust record to update the media asset.

[0276] After the reliability information is obtained, files associated with the media asset can be generated based on the reliability information. The files can be used and viewed by the user and can be used as reliability determination criteria when determining the reliability level. The following provides an explanation.

[0277] FIG. 13 shows the procedure of a data processing method according to an embodiment of the present application. The data processing method includes the following steps.

[0278] 1301: Capture media content via a hardware sensor or generate media content via generation software.

[0279] For the description of step 1301, please refer to the description of step 901 in the foregoing embodiment. Similar points will not be described again in this specification.

[0280] 1302: Obtain one or more trust indicators of the media asset, where the media asset includes at least one of the media content, metadata corresponding to the media content, and a trust record corresponding to the media content.

[0281] For a description of step 1302, please refer to the description of step 902 in the previously described embodiment. Similarities will not be described again in this specification.

[0282] 1303: To obtain reliability information, determine whether each reliability metric meets the criteria.

[0283] For a description of step 1303, please refer to the description of step 903 in the previously described embodiment. Similarities will not be described again in this specification.

[0284] 1304: Generate files associated with media assets based on reliability information.

[0285] The associated files may be independently encapsulated files, and the files may be bound to media assets. Users can determine the reliability level of the media content by viewing the files.

[0286] In addition to automatic triggers such as capturing media content via hardware sensors or generating media content via generation software as shown in Figure 9, the trigger for generating reliability information may also be a user-based active trigger. A description is provided below.

[0287] Figure 14 shows the procedure of a data processing method according to one embodiment of this application. The data processing method includes the following steps:

[0288] 1401: Receive a request for reliability assessment of media assets.

[0289] For example, a user may input a reliability evaluation request for media content through a reliability level determination application.

[0290] For example, the client can input a reliability evaluation request for media content through an interface of a cloud service that provides a reliability level determination service.

[0291] The request may include media assets.

[0292] 1402: Obtain one or more confidence metrics for a media asset, where the media asset includes media content, metadata corresponding to the media content, and at least one confidence record corresponding to the media content.

[0293] For a description of step 1402, please refer to the description of step 902 in the previously described embodiment. Similarities will not be described again in this specification.

[0294] 1403: To obtain reliability information, determine whether each reliability metric meets the criteria.

[0295] For a description of step 1403, please refer to the description of step 903 in the previously described embodiment. Similarities will not be described again in this specification.

[0296] 1404: Send reliability information to the client.

[0297] The above-described embodiment describes a process for generating reliability information for a single media asset. Below, we will describe a process for generating reliability information for multiple media assets in a batch. do .

[0298] Figure 15 shows the procedure of a data processing method according to one embodiment of this application. The data processing method includes the following steps:

[0299] 1501: Retrieve instruction information for media assets.

[0300] For example, the instruction information could be a media asset ID. This is because the process involves generating trust reports for multiple media assets in batches, and therefore the corresponding media asset IDs need to be submitted.

[0301] 1502: Based on the instruction information, obtain one or more confidence metrics for the media asset indicated by the instruction information, where the media asset includes media content and at least one of the metadata and confidence records corresponding to the media content.

[0302] 1503: To obtain reliability information, determine whether each reliability metric meets the criteria.

[0303] 1504: Based on the instruction information, add reliability information to the reliability record corresponding to the media asset indicated by the instruction information.

[0304] Based on Figure 15, Figures 16A-1 and 16A-2 illustrate the steps of a data processing method according to one embodiment of the present application. Compared with Figure 15, the flowcharts in Figures 16A-1 and 16A-2 illustrate the process of batch generation of reliability information and updating of media assets by using the relationships between functional modules.

[0305] Figures 16A-1 and 16A-2 specifically include the following steps.

[0306] 1: The generation algorithm is configured to generate media assets, each media asset containing metadata, media content, and a trust record.

[0307] 2: The generation algorithm sends a request to the extraction algorithm for the generation of a trust certificate (including the media asset ID and the corresponding media asset).

[0308] 3. The extraction algorithm uses media assets as input to extract trust metrics from the metadata, trust records, and media content of each media asset, and generates a trust certificate for each media asset based on the extracted trust metrics.

[0309] 4. The extraction algorithm sends the trust certificate to the verification algorithm in the format (media asset ID, corresponding trust certificate).

[0310] 5. The verification algorithm triggers the verification process.

[0311] 6. The verification algorithm requests that a trust profile be retrieved from the trust file storage side, and the trust profile is predefined by an entity such as a government, platform, or user for a specific scenario.

[0312] 7. The trusted file storage side passes the trust profile to the verification algorithm.

[0313] 8. The verification algorithm uses the trust certificate and the trust profile for each media asset as input to evaluate whether the trust metrics in the trust certificate corresponding to each media asset meet the predefined trust profile, and generates a trust report based on the evaluation results. The verification algorithm generates a trust report for each media asset based on the evaluation results.

[0314] 9. The verification algorithm generates a new trust manifest for each media asset based on the trust report.

[0315] 10: The verification algorithm returns a trust manifest to the generation algorithm in the format (media asset ID, corresponding new trust manifest).

[0316] 11: The generation algorithm adds a new trust manifest to the trust record in order to update the media asset.

[0317] Figures 6 to 16A-2 illustrate the process of generating or using reliability information. The process of generating and using a certificate of trust is described below, with reference to the attached diagrams.

[0318] Figure 16B shows the procedure of a data processing method according to one embodiment of the present application. This procedure mainly describes the process of generating reliability information. As shown in Figure 16B, the data processing method provided in this embodiment of the present application includes the following steps.

[0319] 1601: Retrieve a media asset, which includes media content, metadata corresponding to the media content, and at least one trust record corresponding to the media content.

[0320] For a description of Step 1601, please refer to the description related to media assets in the embodiments described above. Similarities will not be described again in this specification.

[0321] 1602: Based on the media assets, determine one or more confidence metrics for the media assets.

[0322] For a description of Step 1602, please refer to the description related to the reliability indicators in the embodiments described above. Similarities will not be described again in this specification.

[0323] Confidence metrics can be encapsulated in confidence certificates.

[0324] In possible implementations, trust certificates can be added to the trust record.

[0325] In possible implementations, additional files associated with the media asset may be generated based on the trust certificate.

[0326] The associated files may be independently encapsulated files, and the files may be bound to media assets. Users can determine the reliability level of the media content by viewing the files.

[0327] The reliability metrics for media assets can be obtained by acquiring these metrics.

[0328] Figure 16C shows a method for generating trust records. Based on Figure 6B, this embodiment provides a method for generating trust records. Optionally, the trust records may be metadata. The method for generating trust records includes the following steps:

[0329] S161: Retrieve the first media content.

[0330] For example, the first media content may be media content generated by the first electronic device, or media content received by the first electronic device from another electronic device. For example, if the first media content is an image, the first media content may be an image taken by the first electronic device, a screenshot, an AI-generated image, etc. The method by which the first electronic device generates the image is not limited in this application. Alternatively, the first media content may be an image received by the first electronic device from another electronic device. This is not limited in this application.

[0331] S162: Generate a change record for the first media content, the trust record for the first media content includes initial information for the first media content, hard binding for the first media content, and a first digital signature, the first digital signature being the digital signature of the first data, and the first data being data determined based on at least the initial information and hard binding for the first media content.

[0332] The explanations of the technical terms used in steps S161 and S162 are consistent with the explanations of terms in the embodiments described above. Further details will not be provided herein.

[0333] Figure 17 is a diagram showing the structure of a data processing device according to an embodiment of this application. The device 1700 includes the following modules.

[0334] The acquisition module 1701 is configured to acquire one or more trust metrics for a media asset, where the media asset includes media content and at least one of the following: metadata corresponding to the media content and a trust record corresponding to the media content.

[0335] For a specific description of the acquisition module 1701, please refer to the description of step 601 in the embodiment described above. Further details will not be provided in this specification.

[0336] The processing module 1702 is configured to determine whether one or more confidence metrics meet the metric.

[0337] For a detailed description of the processing module 1702, please refer to the description of step 602 in the embodiment described above. Further details will not be provided in this specification.

[0338] In possible implementations, the acquisition module 1701 is: Obtain the first trust configuration information. Further configured to perform this, the first trust configuration information includes metrics that the media asset must meet.

[0339] In possible implementations, one or more trust metrics are determined based on at least one of the following: media content, metadata corresponding to the media content, and trust records corresponding to the media content.

[0340] In possible implementations, one or more confidence metrics are encapsulated in a confidence certificate.

[0341] In possible implementations, the metrics include data indicating the reliability indicators that the media assets must meet.

[0342] In possible implementations, processing module 1702, To determine whether a confidence metric within a metric is included in one or more confidence metrics, or to determine whether one or more confidence metrics are included in a metric. It is specifically configured to perform the following actions.

[0343] In possible implementations, processing module 1702, To generate reliability information for media assets. Further configured to do so, the reliability information indicates whether one or more reliability metrics meet the metric, and the reliability metrics include parameters that indicate the reliability level of the media asset. In possible implementation forms, processing module 1702, Add reliability information to the trust record. It was further configured to perform the following actions.

[0344] In possible implementations, a trust record includes a trust manifest. Processing module 1702 is, Add reliability information to the reliability manifest. It is specifically configured to perform the following actions.

[0345] In possible implementations, the acquisition module 1701 is: Obtaining instruction information for media assets. It is further configured to do the following: Processing module 1702 is, Based on the instruction information, add reliability information to the reliability record corresponding to the media asset indicated by the instruction information. It is specifically configured to perform the following actions.

[0346] In possible implementations, reliability information is encapsulated in a reliability report.

[0347] In possible implementations, processing module 1702, Generate files associated with media assets based on reliability information. It was further configured to perform the following actions.

[0348] In possible implementations, the first trust configuration information includes one of several trust configuration information, and the different trust configuration information indicates different domain or user metric requirements that the media asset must meet.

[0349] In possible implementations, the action of acquiring one or more confidence metrics for a media asset is triggered by capturing media content via hardware sensors or by generating media content via generation software.

[0350] In possible implementations, before acquiring multiple metrics and profiles of media assets, acquisition module 1701 performs the following: Receiving requests for reliability assessment of media content It was further configured to perform the following actions.

[0351] In possible implementations, media content is at least one of the following: images, videos, or audio.

[0352] In addition, one embodiment of this application further provides a data processing device. The device includes the following modules:

[0353] The acquisition module 1701 is configured to acquire media assets, which include media content and at least one of the following: metadata corresponding to the media content and a trust record corresponding to the media content.

[0354] acquisition For a specific description of module 1701, please refer to the description of step 1601 in the embodiment described above. Further details will not be provided in this specification.

[0355] The processing module 1702 is configured to determine one or more confidence metrics for a media asset, and these one or more confidence metrics are used to evaluate the reliability level of the media asset.

[0356] For a detailed description of the processing module 1702, please refer to the description of step 1602 in the embodiment described above. Further details will not be described again in this specification.

[0357] In possible implementations, one or more confidence metrics are encapsulated in a confidence certificate.

[0358] In possible implementations, one or more trust metrics are determined based on at least one of the following: media content, metadata corresponding to the media content, and trust records corresponding to the media content.

[0359] In possible implementations, one or more confidence metrics are used to determine the reliability information of a media asset, and this reliability information indicates whether one or more confidence metrics meet the metric.

[0360] In possible implementations, the metrics include data indicating the reliability indicators that the media assets must meet.

[0361] In possible implementations, the reliability metric includes parameters that indicate the reliability level of the media asset.

[0362] In possible implementations, media content is at least one of the following: images, videos, or audio.

[0363] The execution device provided in embodiments of this application is described below. Figure 18 is a diagram of the structure of an execution device according to embodiments of this application. The execution device 1800 may specifically be represented as a mobile phone, tablet computer, notebook computer, smart wearable device, etc., but is not limited herein. Specifically, the execution device 1800 includes a receiver 1801, a transmitter 1802, a processor 1803, and memory 1804 (the execution device 1800 may include one or more processors 1803, and in Figure 18, one processor is used as an example). The processor 1803 may include an application processor 18031 and a communication processor 18032. In some embodiments of this application, the receiver 1801, transmitter 1802, processor 1803, and memory 1804 may be connected via a bus or by other means.

[0364] Memory 1804 includes read-only memory and random access memory, and can provide instructions and data to processor 1803. A portion of memory 1804 may further include non-volatile random access memory (NVRAM). Memory 1804 stores the processor and operation instructions, executable modules or data structures, subsets thereof, or extensions thereof. Operation instructions may include various operation instructions for performing various operations.

[0365] Processor 1803 controls the operation of the execution device. In certain applications, the components of the execution device are coupled to each other via a bus system. In addition to the data bus, the bus system may further include a power bus, control bus, status signal bus, etc. However, for the sake of clarity, the various types of buses in the diagram are referred to as the bus system.

[0366] The methods disclosed in the embodiments described herein may be applied to or implemented by a processor 1803. The processor 1803 may be an integrated circuit chip having signal processing capabilities. In the implementation process, the steps of the methods described herein may be completed by hardware integrated logic circuits within the processor 1803 or through instructions in the form of software. The processor 1803 may be a general-purpose processor, a digital signal processor (DSP), a microprocessor, or a microcontroller, or may further include an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or another programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The processor 1803 may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments described herein. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc. The steps in the methods disclosed with reference to embodiments of this application may be performed and completed directly by a hardware decoding processor, or by using a combination of hardware and software modules within the decoding processor. The software modules may reside in mature storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The storage media resides in memory 1804. Processor 1803 reads information from memory 1804 and, in combination with the hardware of processor 1803, completes the steps in the aforementioned methods.

[0367] The receiver 1801 may be configured to receive input numerical or character information and generate signal inputs related to the settings and function control of the execution device. The transmitter 1802 may be configured to output numerical or character information. The transmitter 1802 may be further configured to send commands to the disk group to modify the data in the disk group.

[0368] In this embodiment of the present application, the processor 1803 is configured in some cases to perform steps of the data processing method in the embodiment corresponding to Figures 6 to 16A-2.

[0369] One embodiment of the present application further provides a server. Figure 19 is a diagram of the structure of a server according to one embodiment of the present application. Specifically, the server 1900 is implemented by one or more servers. The server 1900 may vary considerably due to different configurations or performance and may include one or more central processing units (CPUs) 1919 (e.g., one or more processors) and one or more storage media 1930 (e.g., one or more mass storage devices) for storing memory 1932, applications 1942, or data 1944. The memory 1932 and the storage media 1930 may be used for temporary or persistent storage. A program stored in the storage media 1930 may include one or more modules (not shown), each module may include a set of instruction operations for the server. Furthermore, the central processing unit 1919 may be configured to communicate with the storage media 1930 and execute a set of instruction operations in the storage media 1930 on the server 1900.

[0370] Server 1900 has one or more power supplies 19 20The system may further include one or more wired or wireless network interfaces 1950, one or more input / output interfaces 1958, or one or more operating systems 1941, such as Windows Server®, Mac OS X®, Unix®, Linux®, and FreeBSD®.

[0371] In this embodiment of the present application, the central processing unit 1919 is configured to perform the steps of the data processing method in the embodiment corresponding to Figures 6 to 16A-2.

[0372] Embodiments of this application further provide a computer program product that includes computer-readable instructions. When computer-readable instructions are executed on a computer, the computer becomes capable of performing steps performed by an execution device, or the computer becomes capable of performing steps performed by a training device.

[0373] One embodiment of this application further provides a computer-readable storage medium. The computer-readable storage medium stores a program for processing signals, and when the program is executed on a computer, the computer is able to perform steps performed by an execution device, or the computer is able to perform steps performed by a training device.

[0374] Furthermore, it should be noted that the described apparatus embodiments are merely examples. Units described as separate parts may or may not be physically separate, and parts shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of the modules may be selected according to the actual needs to achieve the objectives of the solutions of the embodiments. In addition, in the accompanying drawings of the apparatus embodiments provided in this application, the connection relationships between modules indicate that the modules are communicating with one another, which may be specifically implemented as one or more communication buses or signal cables.

[0375] Based on the above-described implementation configurations, those skilled in the art will clearly understand that the present application can be implemented by software in addition to the necessary general-purpose hardware, or by dedicated hardware including dedicated integrated circuits, dedicated CPUs, dedicated memory, dedicated components, etc. In general, any function that can be performed by a computer program can be easily implemented by using the corresponding hardware. Furthermore, the specific hardware structure used to achieve the same function may take various forms, for example, analog circuits, digital circuits, or dedicated circuits. However, with respect to this application, implementation by a software program is, in most cases, a better implementation. Based on such understanding, the technical solution of the present application, either essentially or in part with respect to the prior art, can be implemented in the form of a software product. The computer software product is stored on a readable storage medium such as a computer floppy disk, USB flash drive, removable hard disk, ROM, RAM, magnetic disk, or optical disk, and includes several instructions for instructing a computer device (which may be a personal computer, training device, network device, etc.) to perform the method in the embodiments of the present application.

[0376] All or part of the embodiments described above may be implemented using software, hardware, firmware, or any combination thereof. When software is used to implement an embodiment, all or part of the embodiment may be implemented in the form of a computer program product.

[0377] A computer program product includes one or more computer instructions. When a computer program instruction is loaded onto a computer and executed, all or part of the procedure or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a dedicated computer, a computer network, or another programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center by a wired (e.g., coaxial cable, optical fiber, or digital subscriber line (DSL)) or wireless (e.g., infrared, radio, or microwave) method. The computer-readable storage medium may be any available medium accessible by a computer, or a data storage device that integrates one or more available media, such as a training device or a data center. The usable media may include magnetic media (e.g., floppy disks, hard disks, or magnetic tapes), optical media (e.g., DVDs), and semiconductor media (e.g., solid-state disks (SSDs)).

Claims

1. A data processing method, A step of acquiring a media asset, wherein the media asset includes media content, and the media asset further includes at least one of the metadata of the media content and the trust record of the media content. A step of determining one or more reliability metrics for the media asset, wherein the one or more reliability metrics are used to evaluate the reliability level of the media asset. A method that includes this.

2. A step of obtaining a confidence certificate based on one or more confidence indicators of the media asset, wherein the one or more confidence indicators are encapsulated in the confidence certificate. The method according to claim 1, further comprising:

3. The method according to claim 1 or 2, wherein the one or more confidence metrics are determined based on at least one of the media content, the metadata of the media content, and the confidence record of the media content.

4. The method according to any one of claims 1 to 3, wherein the one or more confidence metrics are used to determine the reliability information of the media asset, and the reliability information indicates whether the one or more confidence metrics satisfy a metric.

5. The method according to any one of claims 1 to 4, wherein the metric includes data indicating a confidence indicator that the media asset must satisfy.

6. The method according to any one of claims 1 to 5, wherein the reliability index includes a parameter indicating the reliability level of the media asset.

7. The method according to any one of claims 1 to 6, wherein the media content is at least one of images, video, or audio.

8. The method according to any one of claims 1 to 7, wherein when the media content is a first media content, the trust record of the media content includes initial information of the first media content, a hard binding of the first media content, and a first digital signature, wherein the first digital signature is a digital signature of first data, and the first data is data determined based on at least the initial information and the hard binding of the first media content.

9. The method according to any one of claims 1 to 8, wherein the initial information includes at least one of the following: the generation time of the first media content, the name of the creator of the first media content, the digital content identifier of the first media content, the generation location of the first media content, information about the device that generates the first media content, the resolution of the first media content, the size of the first media content, the media type of the first media content, copyright information of the first media content, or the method of generating the first media content.

10. A data processing system, A first device configured to acquire media assets, A second device configured to determine one or more confidence indices of the media asset, A third device configured to acquire trust configuration information and determine whether one or more trust metrics of the media asset satisfy the metrics in the trust configuration information, A data processing system equipped with the following features.

11. The data processing system according to claim 10, wherein the second device is further configured to obtain a certificate of confidence based on one or more confidence indicators of the media asset.

12. The data processing system according to claim 10, wherein the third device is further configured to generate a confidence report based on results indicating whether one or more confidence metrics of the media asset satisfy the metrics in the confidence configuration information.

13. A data processing device, An acquisition module configured to acquire a media asset, wherein the media asset includes media content, and the media asset further includes at least one of the metadata of the media content and the trust record of the media content, A processing module configured to determine one or more confidence metrics of the media asset, wherein the one or more confidence metrics are used to evaluate the reliability level of the media asset, and A data processing device equipped with the following features.

14. A data processing device comprising a memory and a processor, wherein the memory stores a code, and the processor is configured to retrieve the code and perform the method according to any one of claims 1 to 9.

15. A computer-readable storage medium containing a computer-readable instruction, wherein when the computer-readable instruction is executed on a computer device, the computer device becomes capable of performing the method according to any one of claims 1 to 9.

16. A computer program product comprising computer-readable instructions, wherein when the computer-readable instructions are executed on a computer device, the computer device becomes capable of performing the method according to any one of claims 1 to 9.

17. A chip comprising a processor, wherein the processor is configured to support a data processing device for carrying out the method according to any one of claims 1 to 9.

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