Image Processing Method and System

Abstract

The present disclosure provides an image processing method and system, which relates to the field of electronic information technology and can solve the problem of terminal devices in cloud desktop systems being secretly photographed when displaying server desktop images. The specific technical solution is: when the server obtains image request information sent by the terminal device, it obtains the desktop image according to the image request information, sends the desktop image to the terminal device, and determines whether it is necessary to blur the desktop image and the target blurring degree according to the target image sent by the terminal device; generates a blurring processing instruction according to the target blurring degree, and sends the blurring processing instruction to the terminal device, thereby preventing the terminal device from being secretly photographed when displaying the desktop image, and does not affect the normal output of the desktop image. The present disclosure is used for image processing when preventing peeping in a cloud desktop system.

Description

Technical Field

[0001] The present disclosure relates to the field of electronic information technology, and in particular to an image processing method and system. Background Art

[0002] Currently, cloud desktop office systems primarily consist of cloud servers and terminal devices. The cloud server assigns a virtual machine to each terminal device, and the terminal device accesses the corresponding application services by connecting to the corresponding virtual machine. Specifically, after the mobile terminal obtains the desktop image of the virtual machine, it operates on the desktop image to generate reverse control instructions. After these reverse control instructions are sent to the virtual machine, the virtual machine performs the corresponding processing locally to operate the desktop. The virtual machine continuously sends the generated desktop images to the mobile terminal in real time, making it feel like the operation is being performed locally for the mobile terminal.

[0003] Currently, this cloud desktop office system has been widely used in specialized industries involving high-security issues due to its high security. This high security is reflected in the fact that no data needs to be stored on the user's local device; all data sources and video images are derived from images in the cloud. Images can also be watermarked in the cloud, ensuring that data cannot be easily leaked.

[0004] However, this security solution that concentrates data in the cloud still cannot solve the problem of using camera devices to secretly photograph the screen. Therefore, the existing anti-spying solution proposes to replace the current screen or directly lock the screen when someone is found to be secretly photographing. However, the effect of this solution is not ideal. The sudden interruption of the screen will bring a bad experience to the user. Moreover, if the original screen is suddenly hidden in some scenarios such as video conferencing, the user may easily miss some important pictures. Summary of the Invention

[0005] The present disclosure provides an image processing method and system that can solve the problem of a terminal device in a desktop system being secretly photographed while displaying a server desktop image. The technical solution is as follows:

[0006] According to a first aspect of an embodiment of the present disclosure, there is provided an image processing method, applied to a server, the method comprising:

[0007] Upon detecting that a terminal device has successfully logged into the server, obtaining target request information and a target image within a target area sent by the terminal device, wherein the target request information is used to request obtaining a desktop image of the server, and the target area includes an area where the terminal device display screen can be captured;

[0008] Acquire the desktop image of the server according to the target request information, and send target coded data corresponding to the desktop image to the terminal device, the target coded data including coded data corresponding to each layer of the desktop image after progressive coding processing;

[0009] When it is determined that the desktop image needs to be blurred based on the target image and the preset policy, a fuzzification processing instruction is generated and sent to the terminal device. The preset policy is determined at least based on the authority level corresponding to the user in the target area or the action corresponding to the user in the target area. The fuzzification processing instruction is used to instruct the desktop image to be blurred based on the target blurring degree, and the fuzzification processing instruction includes the target blurring degree.

[0010] In one embodiment, the server includes an anti-peeping processing module. Before obtaining the target request information sent by the terminal device, the method further includes:

[0011] Receiving login request information sent by a terminal device, the login request information including facial feature information of the login user;

[0012] When the login request information is verified, the target permission level corresponding to the login user is determined according to the preset strategy;

[0013] The anti-peeping processing module establishes a target mapping according to the facial feature information of the logged-in user and the target authority level corresponding to the logged-in user.

[0014] In one embodiment, the server includes the image acquisition module, the image encoding module, and the image sending module. The method of sending the desktop image to the terminal device includes:

[0015] The desktop image of the server collected by the image acquisition module;

[0016] After acquiring the desktop image, the image encoding module divides the desktop image into N image layers;

[0017] According to the progressive coding strategy, the desktop image is coded layer by layer to generate target coded data;

[0018] The image sending module sends the target coded data to the terminal device layer by layer.

[0019] In one embodiment, the server includes an anti-peeping processing module. Before generating the obfuscation processing instruction, the method further includes:

[0020] After the anti-peeping processing module obtains the target image, it performs face recognition processing on the target image to obtain the facial feature information of the user in the target area;

[0021] Determining whether the users in the target area include other users besides the legitimate user based on the facial feature information of the legitimate user and the facial feature information of the users in the target area, where the legitimate users include at least the logged-in user;

[0022] When the users in the target area include other users except the legal user, obtaining the first permission information corresponding to the other users and the target permission information corresponding to the legal user;

[0023] When the first permission information is greater than the target permission level, determining not to perform obfuscation processing and notifying the terminal device;

[0024] When the first permission information is less than the target permission level, it is determined to perform fuzzification processing and obtain a target fuzzification degree, where the target fuzzification degree matches the target number of decoding layers.

[0025] In one embodiment, the method further comprises:

[0026] When the anti-peeping processing module identifies, based on the target image, that the users in the target area include the legal user, the legal user is assigned a cancellation permission, where the cancellation permission is used to indicate permission to cancel the blurring operation of the desktop image;

[0027] When it is detected that the legal user activates the cancellation authority, the cancellation instruction is sent to the terminal device, where the cancellation instruction is used to instruct to cancel the blurring operation of the desktop image, and the cancellation instruction includes the identification information of the legal user.

[0028] According to a second aspect of an embodiment of the present disclosure, there is provided an image processing method, applied to a terminal device, comprising:

[0029] When a successful login to the server is detected, target request information and a target image within a target area are obtained, and the target request information and the target image are sent to the server, where the target area includes an area where the display screen of the terminal device can be photographed;

[0030] According to the target request information, obtaining target coded data corresponding to the desktop image sent by the server, the target coded data including coded data corresponding to each layer of the desktop image;

[0031] When a blur processing instruction is received from the server according to the target image, the desktop image is blurred in response to the blur processing instruction. The blur processing instruction is used to instruct the desktop image to be blurred according to the target blur degree. The blur processing includes at least progressive decoding processing.

[0032] In one embodiment, the terminal device includes an image acquisition module. Before acquiring the target request information and the target image within the target area, the method further includes:

[0033] When the terminal user successfully logs in to the server for the first time, the collection guide information sent by the server is obtained, and the collection guide information is used to prompt the logged-in user to input the user's facial feature information;

[0034] The image acquisition module obtains the facial feature information of the logged-in user according to the acquisition guide information and sends it to the server.

[0035] In one embodiment, the terminal device includes a receiving module, an image decoding module, and an image display module.

[0036] The receiving module receives the target coded data sent by the server;

[0037] Sending the target coded data to the image decoding module;

[0038] The image decoding module parses the blur processing instruction to obtain the target blur level.

[0039] According to the preset processing rules, the target decoding layer number corresponding to the target fuzzification degree is determined.

[0040] According to the target number of decoding layers, the encoded data is decoded layer by layer to obtain an image corresponding to the target number of decoding layers;

[0041] The image display module displays the image corresponding to the target decoding layer number.

[0042] In one embodiment, the method further comprises:

[0043] The receiving module receives a cancel instruction, the cancel instruction is used to instruct to cancel the blurring operation of the desktop image, and the cancel instruction includes identification information of the logged-in user;

[0044] The image decoding module responds to the cancel instruction, cancels the blurring process on the desktop image, decodes the coded data of all layers in the target coded data, and obtains images of all layers in the desktop image;

[0045] The image display module displays images of all layers in the desktop image.

[0046] According to a third aspect of an embodiment of the present disclosure, there is provided an image processing system, including:

[0047] at least one terminal device and server;

[0048] The terminal device includes an image acquisition module, a receiving module, an image decoding module and an image display module;

[0049] The server includes an anti-peeping processing module, an image acquisition module, an image encoding module and an image sending module.

[0050] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0052] Figure 1 is a flowchart of an image processing method provided by an embodiment of the present disclosure;

[0053] Figure 1a is a schematic diagram of a corresponding relationship in an image processing method provided by an embodiment of the present disclosure;

[0054] Figure 1b This is a coding schematic diagram of an image processing method provided by an embodiment of the present disclosure. Figure 1 ;

[0055] Figure 1c This is a coding schematic diagram of an image processing method provided by an embodiment of the present disclosure. Figure 2 ;

[0056] Figure 2 is a flowchart of an image processing method provided by an embodiment of the present disclosure;

[0057] Figure 3 is a structural diagram of an image processing system provided by an embodiment of the present disclosure;

[0058] Figure 3a is a structural diagram of a terminal device in an image processing system provided by an embodiment of the present disclosure;

[0059] Figure 3b It is a structural diagram of a server in an image processing system provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0061] Example 1

[0062] The present disclosure provides an image processing method, which is applied to a server, such as Figure 1 As shown, the image processing method includes the following steps:

[0063] 101. When it is detected that the terminal device has successfully logged into the server, the target request information and the target image within the target area sent by the terminal device are obtained.

[0064] The target request information is used to request to obtain the desktop image of the server.

[0065] The method provided by this disclosure is applied to a cloud desktop system. A user logs into the cloud desktop office system through a zero terminal. After successful login, the cloud server sends the desktop image corresponding to the current user to the current zero terminal. The user can then reverse the operation of the zero terminal to control the desktop.

[0066] In the method provided by the present disclosure, the target image of the target area includes an area opposite to the target display screen of the desktop image sent by the display server, wherein the target area includes an area capable of capturing the display screen of the terminal device, which can monitor the usage environment of the terminal device, such as capturing the user using the terminal device.

[0067] In a specific application, the facial information of the user in the target area can be collected through the camera on the display screen of the zero terminal or the image acquisition device connected to the zero terminal to generate a target image.

[0068] In actual practice, there is no strict order restriction for the terminal device to send the target request information and the target image in the target area. For example, the target image can be sent first, and after the server determines that there is a candid action in the target image, it may not respond to the target request information.

[0069] The method provided by the present disclosure further includes, before detecting that the terminal device has successfully logged into the server, verifying the logged-in terminal device and obtaining facial feature information of the user logged into the terminal device. Specifically, the facial feature information can be processed by an anti-peeping processing module included in the server, including:

[0070] Receiving login request information sent by a terminal device, the login request information including facial feature information of the login user;

[0071] When the login request information is verified, the target permission level corresponding to the login user is determined according to the preset strategy;

[0072] The anti-peeping processing module establishes a target mapping according to the facial feature information of the logged-in user and the target authority level corresponding to the logged-in user.

[0073] In the above steps, when the terminal device logs in to the server for the first time, if the user is logging in for the first time, the system will guide the user to collect personal facial information and inform the user of the collection purpose and authorization of relevant permissions. If the user does not agree, he will not be able to operate the relevant applications or files on the cloud server.

[0074] When the user's facial features are obtained through the terminal device, the camera on the terminal display screen collects the user's facial information and informs the user whether to upload it to the anti-peeping processing module in the cloud server for data processing. After the user data is uploaded, the anti-peeping processing module will create a permission level list for relevant personnel based on the user's identity information (departmental authority).

[0075] In the above steps, after the terminal device completes collecting the user's facial information, it is uploaded to the server and specifically transmitted to the server's anti-peeping processing module. The anti-peeping processing module then matches the user's permission information with the facial information. The user's permission information is determined based on the user's identity information. Typically, different departments and positions have different permissions. Therefore, the user's permission information can be determined based on the user's identity information.

[0076] By establishing corresponding permission levels for different users, it can be achieved that: during the anti-peeping detection process, if someone passes by but does not take any photos, only the screen of people with a lower permission level than the current user will be blurred, thus avoiding unnecessary blurring, improving the accuracy of blurring, and improving the efficiency of anti-peeping processing.

[0077] The server provided by the present disclosure may include a cloud server, and the terminal device may include a zero terminal.

[0078] 102. Acquire the desktop image of the server according to the target request information, and send target coded data corresponding to the desktop image to the terminal device.

[0079] The target coding data in the method provided by the present disclosure includes coding data corresponding to each layer of the desktop image after progressive coding processing.

[0080] In the method provided by the present disclosure, the server includes the image acquisition module, the image encoding module and the image sending module. The method sends the desktop image to the terminal device, including:

[0081] The desktop image captured by the image acquisition module;

[0082] The image encoding module receives a desktop image and divides the desktop image into N image layers;

[0083] The image encoding module performs layer-by-layer encoding processing on the desktop image according to a progressive encoding strategy to generate target encoding data;

[0084] The image sending module sends the target coded data to the terminal device layer by layer.

[0085] Specifically, the encoding method provided by the present disclosure can adopt progressive encoding. The encoding end corresponding to the server always encodes normally according to the progressive encoding method, that is, sends complete progressive encoding data, and when blurring processing is required, notifies the terminal device to perform image blurring processing, that is, the terminal device decides which layer of image data in the encoded data to decode and process.

[0086] In specific deployment, the number of coding layers can be divided into 16 layers in progressive coding, and a corresponding relationship can be established between the number of decoding layers and the target blurring degree in the anti-peeping processing method, that is, the corresponding relationship between the target blurring degree and the target decoding layer number can be determined based on Figure 1a Sure.

[0087] In the method provided by the present disclosure, after acquiring the target frame image, the target frame image is divided into N image blocks. For example, the desktop image can be split into multiple image blocks of 8x8 pixels.

[0088] After dividing the desktop image into N blocks, the present invention also performs a DCT transform on these N blocks: each initial block consists of 64 amplitude values representing specific components of the sample signal. This amplitude is a function of the two-dimensional spatial coordinates and can be represented by a = f(x, y), where x and y are two two-dimensional spatial vectors. After the DCT transform, this function becomes c = g(Fx, Fy), where Fx and Fy are the spatial frequencies in each direction, respectively. The result is another square matrix of 64 values, but each value represents a DCT coefficient. Thus, after the 8x8 DCT forward transform, the 8x8 pixel values are transformed into 8x8 DCT coefficients.

[0089] At the same time, to achieve compression, image data must be quantized after being converted into DCT frequency coefficients. For example, by leveraging the visual characteristics of the human eye, low-frequency components with greater energy in the image are assigned smaller quantization intervals and fewer bits to achieve a higher compression ratio.

[0090] It should be noted that the storage order of the DCT coefficients in the 8*8 matrix reflects, to a certain extent, the importance of the pixels corresponding to the DCT coefficients.

[0091] Perform DCT transformation and quantization processing on each of the N image blocks to obtain a target DCT coefficient value corresponding to each of the N image blocks; and divide the N image blocks into M layers by performing segmentation processing on the target DCT coefficient value corresponding to each image block.

[0092] Here are some specific examples to illustrate:

[0093] If based on Figure 1b The digital serial numbers corresponding to the 64 DCT coefficient values in the image are used to transmit the 64 DCT coefficient values in segments. It can be understood that segmented transmission means that only a part of the DCT coefficients is transmitted each time.

[0094] The order in which the transform coefficients are stored represents their importance to a certain extent; the progressive approach is to transmit these 64 consecutive values in segments, and only need to fill the untransmitted positions with zeros at the decoding end, and then perform inverse quantization and inverse DCT transformation. In this disclosure, the above DCT coefficients are divided into 16 layers, and the specific layering principle is as follows: Figure 1c As shown:

[0095] It should be noted that the above segmentation method is only an example, and the specific segmentation method can be determined according to actual conditions.

[0096] Reference Figure 1c The specific stratification is as follows:

[0097] The DC coefficient corresponding to the first layer is: 0;

[0098] The DC coefficients corresponding to the second layer are: 1, 2;

[0099] The DC coefficients corresponding to the third layer are: 3, 4;

[0100] The DC coefficients corresponding to the 4th layer are: 5, 6, 7;

[0101] The DC coefficients corresponding to the 5th layer are: 8, 9, 10;

[0102] The DC coefficients corresponding to the 6th layer are: 11, 12, 13, 14, 15;

[0103] The DC coefficients corresponding to the 7th layer are: 16, 17, 18, 19, 20;

[0104] The DC coefficients corresponding to the 8th layer are: 21, 22, 23, 24, 25;

[0105] The DC coefficients corresponding to the 9th layer are: 26, 27, 28;

[0106] The DC coefficients corresponding to the 10th layer are: 29, 30, 31;

[0107] The DC coefficients corresponding to the 11th layer are: 32, 33, 34, 35, 36, 37;

[0108] The DC coefficients corresponding to the 12th layer are: 38, 39, 40, 41, 42, 43;

[0109] The DC coefficients corresponding to the 13th layer are: 44, 45, 46, 47, 48, 49;

[0110] The DC coefficients corresponding to the 14th layer are: 50, 51, 52, 53, 54, 55;

[0111] The DC coefficients corresponding to the 15th layer are: 56, 57, 58, 59;

[0112] The DC coefficients corresponding to the 16th layer are: 60, 61, 62, 63.

[0113] Among them, the lower the number of layers, the blurrier the picture, and the higher the number of layers, the clearer it is.

[0114] 103. When it is determined that the desktop image needs to be fuzzy processed based on the target image and the preset policy, a fuzzy processing instruction is generated and sent to the terminal device.

[0115] The fuzzification processing instruction is used to instruct to fuzzy process the desktop image according to a target fuzzification degree.

[0116] The preset strategy is determined at least according to the authority level corresponding to the users in the target area or the actions corresponding to the users in the target area.

[0117] In the method provided by the present disclosure, when determining whether to perform target blurring processing on the desktop image, the determination may be made according to a preset policy, wherein the preset policy is determined at least based on the permission level corresponding to the user in the target area or the action corresponding to the user in the target area. Of course, in actual applications, other preset policies may be included. Here, the following is explained from the perspectives of user and action:

[0118] Example 1: Preset policy based on user permission level:

[0119] The method provided by the present disclosure further includes, before finding the target blurring degree corresponding to the desktop image:

[0120] After the anti-peeping processing module obtains the target image, it performs face recognition processing on the target image to obtain the facial feature information of the user in the target area;

[0121] Determining whether the users in the target area include other users besides the legitimate user based on the facial feature information of the legitimate user and the facial feature information of users in the target area;

[0122] When the users in the target area include other users except the legal user, obtaining the first permission information corresponding to the other users and the target permission information corresponding to the legal user;

[0123] When the first permission information is greater than the target permission level, determining not to perform obfuscation processing and notifying the terminal device;

[0124] When the first permission information is less than the target permission level, it is determined to perform fuzzification processing and determine a target fuzzification degree, and the target fuzzification degree matches the target number of decoding layers.

[0125] The above-mentioned legal users include at least logged-in users, and may also be legal users determined by the server based on the user's facial feature information.

[0126] To further optimize the above solution, during the blurring process, if the server detects that the legitimate user is also in the current image (that is, the legitimate user is also in front of the screen) through face recognition, the current blurring process can be canceled. Specifically, the following steps are performed:

[0127] After the anti-peeping recognition module obtains the target image, it performs face recognition processing on the target image to obtain the facial feature information of the user in the target area;

[0128] Determining whether the users in the target area include the legitimate user based on the facial feature information of the legitimate user and the facial feature information of the users in the target area;

[0129] When the anti-peeping processing module identifies, based on the target image, that the users in the target area include the legal user, the legal user is assigned a cancellation permission, where the cancellation permission is used to indicate permission to cancel the blurring operation of the desktop image;

[0130] When it is detected that the legal user activates the cancellation authority, the cancellation instruction is sent to the terminal device, where the cancellation instruction is used to instruct to cancel the blurring operation of the desktop image, and the cancellation instruction includes the identification information of the legal user.

[0131] Specifically, during the practical deployment process, if the server detects through facial recognition that a legitimate user is also in the current image (that is, the legitimate user is also in front of the display screen) during the blurring process, it can enable a target button, which is used to cancel the current blurring process. That is, the legitimate user is allowed to cancel the current blurring process through a preset button or shortcut key, thereby facilitating the user's viewing needs in special circumstances. If the user triggers the operation to cancel the blurring process, the server records the current image captured by the zero-terminal camera and the current desktop display image in real time, and notifies the administrator. The purpose of this step is to facilitate the preservation of the scene and subsequent verification of the scene, or to find the person responsible when a problem occurs.

[0132] Example 2: Preset strategy based on user actions:

[0133] If the action in the target image is suspected to be a candid photo shoot, blurring is performed. Specifically, the degree of blurring can be determined based on the degree of suspicion of the action, such as 100% blurring.

[0134] Specifically, it is possible to determine whether there is any suspected candid photography behavior based on the actions of other people, whether objects similar to camera devices can be detected in the picture, the angle of the camera device, etc.

[0135] The image processing method provided by the embodiment of the present disclosure is applied to a server. When the server obtains image request information sent by a terminal device, it obtains a desktop image and a target image sent by the terminal device according to the image request information; based on the target image, it determines whether the desktop image needs to be blurred and the target blurring degree; generates a blurring processing instruction according to the target blurring degree, and sends the blurring processing instruction to the terminal device, thereby preventing the terminal device from being secretly photographed when displaying the desktop image, and does not affect the normal output of the desktop image.

[0136] This approach effectively prevents illegal peeping on the server's desktop image from being displayed by the terminal device, and avoids the problem of directly cutting off or replacing the current screen to prevent peeping, which can lead to a poor user experience and the risk of missing some real-time display content. Furthermore, in multi-terminal display scenarios (one cloud server corresponds to multiple terminal devices), local blurring by the zero terminal ensures that only the terminal at risk of leakage is blurred, without affecting the normal display of other zero terminals.

[0137] The method provided by this disclosure utilizes progressive decoding technology to blur the displayed image when a leak risk is detected on the display side. This blurring method is based on progressive layered coding technology. Depending on whether someone is secretly photographing or passing by people with different permissions, the image is blurred to varying degrees. In other words, different layers of image data are decoded, and the higher the number of layers, the clearer the image.

[0138] Example 2

[0139] Based on the above Figure 1 Corresponding to the image processing method provided in the embodiment, another embodiment of the present disclosure provides an image processing method, which can be applied to a terminal device.

[0140] Reference Figure 2 As shown, the image processing method provided in this embodiment includes the following steps:

[0141] 201. When a successful login to the server is detected, target request information and a target image in a target area are obtained, and the target request information and the target image are sent to the server.

[0142] The target image in the method provided by the present disclosure refers to a target image within a target area, and the target area includes an area where the display screen of the terminal device can be photographed.

[0143] The method provided by the present disclosure further includes obtaining the user's facial feature information through the image acquisition module before obtaining the target request information and the target image within the target area:

[0144] Obtain the login information of the logged-in user, generate login request information based on the login information, and send the login request information to the server;

[0145] When the login request information passes the verification, the collection guide information sent by the server is obtained, and the collection guide information is used to prompt the login user to enter the user's facial feature information;

[0146] According to the collection guidance information, the facial feature information of the logged-in user is obtained and sent to the server.

[0147] The terminal device will obtain the target permission level corresponding to the logged-in user sent by the server based on the facial feature information of the logged-in user.

[0148] In a specific application, facial feature information of a logged-in user may be obtained when the user logs into the server for the first time, thereby improving the efficiency of facial recognition, or facial feature information of the user may be obtained periodically, thereby improving the accuracy of facial recognition.

[0149] 202. Obtain the desktop image sent by the server according to the target request information.

[0150] In the method provided by the present disclosure, the desktop image can be obtained through a receiving module. Specifically, the desktop image is target coding data, and the target coding data is coding data corresponding to each image in N image layers in the target image.

[0151] 203. When receiving a fuzzification processing instruction sent by the server according to the target image, respond to the fuzzification processing instruction and perform fuzzification processing on the desktop image.

[0152] The fuzzification processing instruction is used to instruct the desktop image to be fuzzy processed according to a target fuzzification degree, and the fuzzification processing at least includes a progressive decoding process.

[0153] In the method provided by the present disclosure, the desktop image received by the terminal device is encoded data encoded layer by layer, so each layer of image data received needs to be decoded; during the decoding process at the decoding end, the higher the number of layers displayed by the decoding, the clearer the display image.

[0154] Therefore, when the desktop image needs to be blurred, the image blurring process is performed, that is, the terminal device determines the degree of anti-peeping processing and obtains the highest decoding layer for decoding and display. The higher the decoding layer, the clearer the display image.

[0155] The method provided by this disclosure utilizes progressive decoding technology to blur the displayed image when a leak risk is detected on the display side. This blurring method is based on progressive layered coding technology. Depending on whether someone is secretly photographing or passing by people with different permissions, the image is blurred to varying degrees. In other words, different layers of image data are decoded, and the higher the number of layers, the clearer the image.

[0156] In determining the specific correspondence between the target fuzzification degree and the target decoding layer number, we can use Figure 1a Sure.

[0157] In an optional embodiment, the terminal device includes a receiving module, an image decoding module, and an image display module, and the desktop image is blurred by the above modules:

[0158] receiving, by the receiving module, target coded data sent by the server, the target coded data being coded data corresponding to each layer in the N image layers in the desktop image;

[0159] Sending the target coded data to the image decoding module;

[0160] The image decoding module parses the blur processing instruction to obtain the target blur degree;

[0161] According to the preset processing rules, the target decoding layer number corresponding to the target blurring degree is determined;

[0162] According to the target number of decoding layers, the encoded data is decoded layer by layer to obtain an image corresponding to the target number of decoding layers;

[0163] The image display module displays the image corresponding to the target decoding layer number.

[0164] A specific example is given here for explanation: when the received target blur level is 50%, it is determined that the target decoding layers corresponding to the target blur level are layers 1 to 6, so the data of layers 1 to 6 are decoded and displayed, and the data of other layers are not displayed;

[0165] Alternatively, when the received target blur level is 100%, it is determined that the target decoding layer number corresponding to the blur level is the first layer, and only the decoded first layer data is displayed, and other layer data are not displayed.

[0166] Preferably, when the received target blur level is 100% (that is, the server detects a risk of being secretly photographed), a warning prompt may be popped up at a preset position on the screen to indicate that there is a risk of being secretly photographed.

[0167] The method provided by the present disclosure also cancels the blurring process based on the user in the target image, specifically including:

[0168] The receiving module receives a cancel instruction, the cancel instruction is used to instruct to cancel the blurring operation of the desktop image, and the cancel instruction includes identification information of the logged-in user;

[0169] The image decoding module responds to the cancel instruction, cancels the blurring process on the desktop image, decodes the coded data of all layers in the target coded data, and obtains images of all layers in the desktop image;

[0170] The image display module displays images of all layers in the desktop image.

[0171] The image processing method provided by the embodiment of the present disclosure is applied to a terminal device. After the terminal device successfully logs in to the server, it obtains the desktop image sent by the server and sends a target image to the server. When a blur processing instruction sent by the server is received based on the target image, the desktop image is blurred according to the target blurring degree in the blur processing instruction, thereby preventing the terminal device from being secretly photographed when displaying the desktop image and does not affect the normal output of the desktop image.

[0172] This approach effectively prevents illegal peeping on the server's desktop image from being displayed by the terminal device, and avoids the problem of directly cutting off or replacing the current screen to prevent peeping, which can lead to a poor user experience and the risk of missing some real-time display content. Furthermore, in multi-device display scenarios (one cloud server corresponds to multiple terminals), local blurring by the terminal ensures that only the terminal at risk of leakage is blurred, without affecting the normal display of other zero terminals.

[0173] The method provided by this disclosure utilizes progressive decoding technology to blur the displayed image when a leak risk is detected on the display side. This blurring method is based on progressive layered coding technology. Depending on whether someone is secretly photographing or passing by people with different permissions, the image is blurred to varying degrees. In other words, different layers of image data are decoded, and the higher the number of layers, the clearer the image.

[0174] Example 3

[0175] Based on the above Figure 1 and Figure 2 The image processing method described in the corresponding embodiment is as follows: an embodiment of the system of the present disclosure, which can be used to execute the embodiment of the method of the present disclosure.

[0176] The present disclosure provides an image processing system. Figure 3 As shown, the image processing system 30 includes: at least one terminal device 301 and a server 302;

[0177] The terminal device 301 can be used to execute any one of the above-mentioned image processing methods applied to the terminal device 302, such as Figure 2 and the method in Example 2;

[0178] The server 302 is used to execute any one of the above-mentioned image processing methods applied to the server 302, such as Figure 1 and the method in Example 1.

[0179] The image processing system 30 provided in the embodiment of the present disclosure includes at least one terminal device 301 and a server 302;

[0180] The terminal device 301 includes an image acquisition module 3011, a receiving module 3012, an image decoding module 3013 and an image display module 3014;

[0181] The server 302 includes an image acquisition module 3021, an image encoding module 3022, an image sending module 3023 and an anti-peeping processing module 3024;

[0182] This system 30 effectively prevents illegal peeping of the server's desktop image by the terminal device, and also avoids the problem of directly cutting off or replacing the current screen to prevent peeping, which leads to a poor user experience and the risk of missing some real-time displayed content. On the other hand, in a multi-terminal display application scenario (one cloud server corresponds to multiple terminals), the terminal performs the blurring locally, ensuring that only the terminal with a leakage risk is blurred, without affecting the normal display of other zero terminals.

[0183] Based on the above Figure 1 and Figure 2 In accordance with the image processing method described in the embodiment, the present disclosure also provides a computer-readable storage medium. For example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, or an optical data storage device. The storage medium stores computer instructions for executing the above-mentioned Figure 1 and Figure 2 The image processing method described in the corresponding embodiment will not be repeated here.

[0184] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

Claims

1. An image processing method, characterized in that: Applied to a server, the method includes: Upon detecting that a terminal device has successfully logged into the server, obtaining target request information and a target image within a target area sent by the terminal device, wherein the target request information is used to request obtaining a desktop image of the server, and the target area includes an area where the terminal device display screen can be captured; Acquire the desktop image of the server according to the target request information, and send target coded data corresponding to the desktop image to the terminal device, the target coded data including coded data corresponding to each layer of the desktop image after progressive coding processing; When it is determined, based on the target image and a preset policy, that the desktop image requires blurring, a blurring instruction is generated and sent to the terminal device, wherein the preset policy is determined based on at least an authority level corresponding to a user in the target area or an action corresponding to a user in the target area, and the blurring instruction is used to instruct blurring the desktop image according to a target blurring degree, and the blurring instruction includes a target blurring degree; The server includes an anti-peeping processing module. Before obtaining the target request information sent by the terminal device, the method further includes: Receiving login request information sent by a terminal device, wherein the login request information includes facial feature information of the logging-in user; When the login request information passes the verification, the target permission level corresponding to the login user is determined according to the preset policy; The anti-peeping processing module establishes a target mapping according to the facial feature information of the logged-in user and the target authority level corresponding to the logged-in user; Before generating the fuzzification processing instruction, the method further includes: After the anti-peeping processing module obtains the target image, it performs face recognition processing on the target image to obtain facial feature information of the user in the target area; Determine, based on facial feature information of legitimate users and facial feature information of users in the target area, whether the users in the target area include other users except the logged-in user, wherein the legitimate users include at least the logged-in user; When the users in the target area include other users except the legal user, obtaining first permission information corresponding to the other users and target permission information corresponding to the legal user; When the first permission information is greater than the target permission level, determining not to perform obfuscation processing and notifying the terminal device; When the first permission information is less than the target permission level, it is determined to perform fuzzification processing and obtain a target fuzzification degree, where the target fuzzification degree matches the target number of decoding layers.

2. The method according to claim 1, characterized in that The server includes an image acquisition module, an image encoding module, and an image sending module. The method of sending the desktop image to the terminal device includes: The desktop image of the server collected by the image collection module; After acquiring the desktop image, the image encoding module divides the desktop image into N image layers; According to a progressive encoding strategy, encoding the desktop image layer by layer to generate target encoding data; The image sending module sends the target coded data to the terminal device layer by layer.

3. The method according to claim 1, characterized in that The method further comprises: When the anti-peeping processing module identifies that the users in the target area include a legitimate user based on the target image, the legitimate user is assigned a cancellation permission, where the cancellation permission is used to indicate permission to cancel the blurring operation of the desktop image; When it is detected that the legal user activates the cancellation authority, a cancellation instruction is sent to the terminal device, where the cancellation instruction is used to instruct to cancel the blurring operation of the desktop image, and the cancellation instruction includes identification information of the legal user.

4. An image processing method, characterized in that: Applied to a terminal device, the method includes: Upon detecting successful login to the server, obtaining target request information and a target image within a target area, and sending the target request information and the target image to the server, wherein the target request information is used to request obtaining a desktop image of the server, and the target area includes an area where the display screen of the terminal device can be captured; According to the target request information, obtaining target coding data corresponding to the desktop image sent by the server, the target coding data including coding data corresponding to each layer of the desktop image; When receiving a blur processing instruction sent by the server according to the target image, responding to the blur processing instruction, blurring the desktop image, wherein the blur processing instruction is used to instruct to blur the desktop image according to a target blurring degree, and the blurring processing at least includes progressive decoding processing; The method further comprises: The receiving module receives a cancel instruction, wherein the cancel instruction is used to instruct to cancel the blurring operation of the desktop image, and the cancel instruction includes identification information of a valid user; The image decoding module responds to the cancel instruction, cancels the blurring process on the desktop image, decodes the coded data of all layers in the target coded data, and obtains images of all layers in the desktop image; The image display module displays images of all layers in the desktop image.

5. The method according to claim 4, characterized in that The terminal device includes an image acquisition module. Before acquiring the target request information and the target image within the target area, the method further includes: When a terminal user successfully logs in to the server for the first time, obtaining collection guidance information sent by the server, wherein the collection guidance information is used to prompt the logged-in user to input the user's facial feature information; The image acquisition module obtains the facial feature information of the logged-in user according to the acquisition guide information and sends it to the server.

6. The method according to claim 4, characterized in that The terminal device includes a receiving module, an image decoding module, and an image display module. The receiving module receives the target coded data sent by the server; Sending the target coded data to the image decoding module; The image decoding module parses the blur processing instruction to obtain the target blur level. According to the preset processing rules, the target decoding layer number corresponding to the target blurring degree is determined. According to the target number of decoding layers, the encoded data is decoded layer by layer to obtain an image corresponding to the target number of decoding layers; The image display module displays the image corresponding to the target decoding layer number.

7. An image processing system, characterized in that: including at least one terminal device and a server; The terminal device includes an image acquisition module, a receiving module, an image decoding module and an image display module; The server includes an anti-peeping processing module, an image acquisition module, an image encoding module and an image sending module; The server is used to execute the method according to any one of claims 1 to 3; The terminal device is used to execute the method according to any one of claims 4 to 6.

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