Computer big data information security risk behavior identification method and system

Abstract

The invention discloses a computer big data information security risk behavior identification method and system, and aims to solve the problem of sensitive information leakage existing in image information transmission in chat software. According to the method, when a user sends a picture to a target contact person, detail information in the picture, such as an environment scene, position information, text content, associated figures and clothes and accessories, is automatically recognized, and a sensitive information prompt list is generated for the user to view. And the user can select to cancel the sending, perform desensitization processing or confirm the sending according to the prompt list, so that the sensitive information is effectively prevented from being leaked. According to the method, the sensitive information in the picture is automatically identified and the user is prompted, so that the safety awareness of the user is enhanced, and the risk of sensitive information leakage is effectively reduced. And meanwhile, flexible desensitization processing options are provided, so that the privacy of the user is protected, and the individual requirements of the user are met.

Description

Technical Field

[0001] This application relates to the technical field of risk identification, and particularly to a method and system for identifying computer big data information security risk behaviors. Background Art

[0002] With the rapid development and popularization of the Internet, telecommunications fraud cases occur frequently, and the fraud means are constantly renovated, bringing serious property losses and security risks to the majority of users. During the process of telecommunications fraud, fraudsters often communicate with the deceived person through chat software for a long time and in multiple rounds to establish trust and induce them to disclose personal information. During this process, users may inadvertently send many pictures or videos containing personal sensitive information.

[0003] Among these image information, there are often some details that are not easily noticed, such as the street view of the home address, partial photos of identity documents, fragments of bank card numbers, backgrounds of private documents, etc. Once these detail information are obtained and utilized by fraudsters, it may lead to serious property losses for users and even threaten the personal safety and privacy of users.

[0004] However, most of the existing chat software lacks an effective sensitive information prompt mechanism in terms of image information transmission. When users send pictures or videos, they often cannot immediately know the sensitive information that may be contained therein, let alone obtain corresponding risk prompts. This "blind area" in information transmission provides an opportunity for fraudsters, making users become victims of fraud unconsciously. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for identifying computer big data information security risk behaviors, which can improve the above problems.

[0006] The embodiments of this application are implemented as follows: In a first aspect, this application provides a method for identifying computer big data information security risk behaviors, which includes the following steps: S1, in response to a sending operation of sending at least one picture to a target contact, identify the detail information in the at least one picture; S2, generate a sensitive information prompt list according to the detail information and display the sensitive information prompt list; After step S2, the method for identifying computer big data information security risk behaviors further includes at least one of the following: In response to a selection operation for target sensitive information in the sensitive information prompt list, cancel the sending operation of the target picture involving the target sensitive information; In response to a selection operation for a target sensitive information in the list of sensitive information prompts, perform desensitization processing on a target picture involving the target sensitive information; In response to a confirmation operation, execute the sending instruction of the at least one picture corresponding to the sending operation.

[0007] It can be understood that the present application proposes a method for identifying computer big data information security risk behaviors, aiming to solve the problem of sensitive information leakage in the transmission of image information in chat software. When a user sends a picture to a target contact, this method automatically identifies the detailed information in the picture, such as the environmental scene, location information, text content, associated people, clothing items, etc., and generates a list of sensitive information prompts for the user to view. The user can choose to cancel sending, perform desensitization processing, or confirm sending according to the prompt list, thus effectively avoiding the leakage of sensitive information. This method enhances the user's security awareness by automatically identifying sensitive information in the picture and effectively reduces the risk of sensitive information leakage. At the same time, flexible desensitization processing options are provided, which not only protect the user's privacy but also meet the user's personalized needs.

[0008] In an optional embodiment of the present application, the identifying the detailed information in the at least one picture includes at least one of the following: Perform detailed identification on each picture to obtain the detailed information in each picture; Perform detailed identification on portrait pictures containing human face elements in the at least one picture to obtain the detailed information of the portrait pictures; Perform detailed identification on text pictures containing text elements in the at least one picture to obtain the detailed information of the text pictures.

[0009] It can be understood that when a user sends pictures to a target contact in batches, from the perspective of accurate detection requirements, detailed identification can be performed on each picture. However, performing detailed identification on each picture requires a large amount of computing power and calculation time. To save computing power, detailed identification operations can be performed only on portrait pictures or text pictures with a relatively high probability of containing sensitive information in the batch of pictures.

[0010] In an optional embodiment of the present application, the detailed identification includes: identifying the environmental scene in the picture and using the environmental scene as detailed information. The detailed identification in this embodiment focuses on the environmental scene in the picture, such as a home living room, a company office, etc. By identifying these scenes, the system can prompt the user about possible leaked residential or work information. For example, if the unique decoration of a home address is shown in the picture, the system can remind the user.

[0011] In an alternative embodiment of the present application, the detail recognition includes: recognizing landmark buildings in the background area of the picture, and using the location information of the landmark buildings as detail information. In this embodiment, landmark buildings in the picture background are recognized to obtain location information. This helps prevent users from inadvertently disclosing their geographical locations. For example, landmark buildings in travel photos may reveal whereabouts. This function enhances users' safety awareness.

[0012] In an alternative embodiment of the present application, the detail recognition includes: recognizing text elements in the picture, understanding the text content corresponding to the text elements, and using the text content as detail information. In this embodiment, the text content in the picture is recognized and understood, such as ID numbers, bank account numbers, etc. The system can automatically detect and prompt these sensitive information to prevent users from accidentally sending them. For example, screenshots containing personal information can be intercepted by the system before being sent. This function effectively reduces the risk of information leakage.

[0013] In an alternative embodiment of the present application, the detail recognition includes: recognizing associated persons in the picture that match the person information stored in the picture library, and using the person information corresponding to the associated persons as detail information. Optionally, the person information includes at least one of the following: the person nicknames stored in the picture library, the person relationship classifications stored in the picture library. In this embodiment, associated persons in the picture that match the person information in the picture library, such as family members and friends, are recognized. The system can prompt the user about the identities and relationships of the persons in the picture to prevent accidental sending to inappropriate contacts. For example, sending a family photo to a stranger may disclose family member information. This function protects the privacy of users' interpersonal relationships.

[0014] In an alternative embodiment of the present application, the detail recognition includes: recognizing the clothing and accessories worn by the associated persons in the picture, and using the product information corresponding to the clothing and accessories as detail information. Optionally, the product information includes at least one of the following: brand name, product model number, product selling price. In this embodiment, the clothing and accessories worn by the persons in the picture, such as famous brand bags and limited edition shoes, are recognized, and their product information is obtained. The system can prompt the user about this information that may disclose economic status or personal preferences. For example, photos showing off wealth may attract criminals. This function helps users share personal information carefully.

[0015] In an alternative embodiment of the present application, generating a sensitive information prompt list based on the detail information includes selecting at least one of the following as sensitive information and generating a sensitive information prompt list: the environmental scene; the location information; the text content; the person information corresponding to the target persons among the associated persons that meet the first preset condition; the product information corresponding to the clothing and accessories that meet the second preset condition.

[0016] Optionally, the first preset condition includes that the classification of the relationship between the persons belongs to a preset classification. In this embodiment, the first preset condition is that the classification of the relationship between the persons belongs to a preset classification, such as "family members". When it is recognized that the associated persons in the picture belong to the family member category, their information is used as sensitive information for prompting. For example, if the user attempts to send a picture containing a family photo to a colleague, the system will prompt sensitive information. This function helps prevent the leakage of family information to inappropriate persons and enhances privacy protection.

[0017] Optionally, the first preset condition includes that the classification of the relationship between the persons is different from the classification of the contacts corresponding to the target contacts. The first preset condition is that the classification of the relationship between the persons is different from the classification of the target contacts. For example, if the user attempts to send a photo of a friend to a family member, the system will prompt. This avoids misinformation being sent to people in different social circles and protects the privacy of the user's social relationships.

[0018] Optionally, the second preset condition includes the clothing and accessories worn by the target person. The second preset condition is the clothing and accessories worn by the target person. The system identifies and prompts specific items worn by the persons in the picture, such as designer clothing. This helps users realize the possible leakage of their economic status or personal preferences and avoid unnecessary showing off or risks.

[0019] Optionally, the second preset condition includes that the brand name belongs to a preset brand set. The second preset condition is that the brand name belongs to a preset brand set. The system identifies and prompts preset high-value brands that appear in the picture, such as luxury brands. This helps users share information about items that may pose security risks with caution.

[0020] Optionally, the second preset condition includes that the selling price of the commodity is greater than a preset price threshold. The second preset condition is that the selling price of the commodity is greater than a preset price threshold. The system identifies and prompts items with higher values in the picture, such as expensive electronic products. This helps users avoid inadvertently disclosing financial information that may attract lawbreakers and enhances their security awareness.

[0021] In an optional embodiment of the present application, the desensitization processing includes at least one of the following: Blurring the image element area corresponding to the target sensitive information; Blurring the person element area corresponding to the target sensitive information; Blurring the text element area corresponding to the target sensitive information.

[0022] In a second aspect, the present application discloses a computer big data information security risk behavior recognition system, including a processor, an input device, an output device, and a memory. The processor, the input device, the output device, and the memory are interconnected. Among them, the memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions to execute the method according to any one of the first aspect.

[0023] In a third aspect, the present application further discloses an electronic device, including: A detail recognition module, configured to recognize detail information in at least one picture in response to a sending operation of sending at least one picture to a target contact; A sensitive information prompt list generation and display module, configured to generate a sensitive information prompt list according to the detail information and display the sensitive information prompt list; A sensitive information processing module, configured to perform at least one of the following: In response to a selection operation for a target sensitive information in the sensitive information prompt list, cancel the sending operation of a target picture involving the target sensitive information; In response to a selection operation for a target sensitive information in the sensitive information prompt list, perform desensitization processing on a target picture involving the target sensitive information; In response to a confirmation operation, execute the sending instruction of the at least one picture corresponding to the sending operation.

[0024] Advantageous effects: The present application proposes a computer big data information security risk behavior recognition method and system, aiming to solve the problem of sensitive information leakage in the transmission of image information in chat software. When the user sends a picture to a target contact, the method automatically recognizes the detail information in the picture, such as the environmental scene, location information, text content, associated people, clothing items, etc., and generates a sensitive information prompt list for the user to view. The user can select to cancel sending, perform desensitization processing, or confirm sending according to the prompt list, thus effectively avoiding sensitive information leakage. By automatically identifying sensitive information in the picture and prompting the user, the method enhances the user's security awareness and effectively reduces the risk of sensitive information leakage. At the same time, flexible desensitization processing options are provided, which not only protect the user's privacy but also meet the user's personalized needs.

[0025] To make the above objects, features, and advantages of the present application more obvious and understandable, the following specific embodiments are given and described in detail in conjunction with the accompanying drawings. Description of the Drawings

[0026] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can also be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a schematic flowchart of a method for identifying computer big data information security risk behaviors provided by the present application; Figure 2 It is a schematic diagram of a sending operation provided by the present application; Figure 3 It is a schematic diagram for detailed identification of a picture provided by the present application; Figure 4 It is a schematic diagram for detailed identification of another picture provided by the present application; Figure 5 It is a schematic diagram of a sensitive information prompt list provided by the present application; Figure 6 It is a schematic diagram of the desensitization processing result provided by the present application. Specific embodiments

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present application.

[0029] In the first aspect, as Figure 1 shown, the present application provides a method for identifying computer big data information security risk behaviors, which includes the following steps: S1. In response to a sending operation of sending at least one picture to a target contact, identify the detailed information in at least one picture. When communicating with netizens through a chat software, the user may send local pictures to the other party, and these pictures may inadvertently disclose important information such as the street view of the home address and the work permit. As Figure 2 shown is a schematic diagram of the scenario of sending pictures when the user communicates with the target contact through the chat software. It can be seen that the user has selected the first and second pictures stored locally. If the user presses the send button, the above "sending operation" will be triggered, and the system device will perform detailed identification in response to this "sending operation".

[0030] S2. Generate a sensitive information prompt list according to the detailed information and display the sensitive information prompt list.

[0031] It can be understood that the present application proposes a method for identifying computer big data information security risk behaviors, aiming to solve the problem of sensitive information leakage in the transmission of image information in chat software. When a user sends a picture to a target contact, this method automatically identifies the detailed information in the picture, such as the environmental scene, location information, text content, associated people, clothing items, etc., and generates a list of sensitive information prompts for the user to view. The user can choose to cancel sending, perform desensitization processing, or confirm sending according to the prompt list, thus effectively avoiding the leakage of sensitive information. By automatically identifying sensitive information in the picture and prompting the user, this method enhances the user's security awareness and effectively reduces the risk of sensitive information leakage. At the same time, flexible desensitization processing options are provided, which not only protect the user's privacy but also meet the user's personalized needs.

[0032] In an optional embodiment of the present application, identifying the detailed information in at least one picture includes at least one of the following: Performing detailed identification on each picture to obtain the detailed information in each picture; Performing detailed identification on portrait pictures containing human face elements in at least one picture to obtain the detailed information of the portrait pictures; Performing detailed identification on text pictures containing text elements in at least one picture to obtain the detailed information of the text pictures.

[0033] It can be understood that when the user sends pictures to a target contact in batches, from the perspective of the need for accurate detection, detailed identification can be performed on each picture. However, performing detailed identification on each picture requires a large amount of computing power and calculation time. To save computing power, detailed identification operations can be performed only on portrait pictures or text pictures with a relatively high probability of containing sensitive information in the batch of pictures.

[0034] In an optional embodiment of the present application, the detailed identification includes: identifying the environmental scene in the picture and taking the environmental scene as the detailed information. The detailed identification in this embodiment focuses on the environmental scene in the picture, such as the living room at home, the office in the company, etc. By identifying these scenes, the system can prompt the user about the possible leaked residential or work information. For example, if the unique decoration of the home address is shown in the picture, the system can remind the user. For the identification of the environmental scene in the picture, the convolutional neural network (CNN) algorithm in deep learning can be used. This algorithm is trained with a large number of labeled environmental scene pictures to learn the feature representations of different scenes. To improve the identification accuracy, a transfer learning strategy can be adopted, using the general features learned by the pre-trained model on a large dataset and then fine-tuning for specific scene data. At the same time, data augmentation techniques, such as rotation, scaling, cropping, etc., are introduced to increase the diversity of the training data and improve the generalization ability of the model.

[0035] In an alternative embodiment of the present application, detail recognition includes: identifying landmark buildings in the background area of the picture and using the location information of the landmark buildings as detail information. In this embodiment, landmark buildings in the picture background are identified to obtain location information. This helps prevent users from inadvertently disclosing their geographical locations. For example, landmark buildings in travel photos may reveal whereabouts. This function enhances users' safety awareness. For the identification of landmark buildings in the background area of the picture, image recognition algorithms in deep learning can be used, such as convolutional neural network (CNN) combined with region-based convolutional neural network (R-CNN) series algorithms. These algorithms can accurately identify landmark buildings in complex backgrounds by learning the features of buildings through training. In implementation, candidate regions in the picture are first extracted using a pre-trained model, and then these regions are classified to identify the landmark buildings. Through a geographic information system (GIS) database or an online map service, the location information of the identified landmark buildings can be obtained, thus helping users prevent the leakage of sensitive information.

[0036] In an alternative embodiment of the present application, detail recognition includes: identifying text elements in the picture, understanding the text content corresponding to the text elements, and using the text content as detail information. In this embodiment, the text content in the picture, such as work permit numbers, ID card numbers, bank account numbers, etc., is identified and understood. The system can automatically detect and prompt these sensitive information to prevent users from accidentally sending them. For example, screenshots containing personal information can be intercepted by the system before being sent. This function effectively reduces the risk of information leakage. For the identification and understanding of text elements in the picture, optical character recognition (OCR) technology can be used, and specific algorithms include CRNN (convolutional neural network + recurrent neural network + connectionist temporal classification) based on deep learning, etc. In implementation, the text features in the image are first extracted using a convolutional neural network, then the text sequence information is processed using a recurrent neural network, and finally the character sequence is decoded into readable text content through connectionist temporal classification. This process can automatically identify and extract text elements in the picture, such as sensitive information like work permit numbers and ID card numbers, effectively preventing users from accidentally sending them and reducing the risk of information leakage.

[0037] In an alternative embodiment of the present application, detail recognition includes: identifying associated persons in the picture that match the person information stored in the local gallery, and using the person information corresponding to the associated persons as detail information. Optionally, the person information includes at least one of the following: the person nicknames stored in the local gallery, the person relationship classifications stored in the local gallery. In this embodiment, associated persons in the picture that match the person information in the local gallery, such as family members and friends, are identified. The system can prompt the user of the identities and relationships of the persons in the picture to prevent accidental sending to inappropriate contacts. For example, sending a family photo to a stranger may disclose family member information. This function protects the privacy of the user's interpersonal relationships. For the recognition of associated persons in the picture, a face recognition algorithm, such as a convolutional neural network (CNN) algorithm based on deep learning, can be used. First, the face regions in the picture are detected and features are extracted, and then these features are compared with the person information stored in the local gallery. The person information stored in the local gallery can include person nicknames, relationship classifications, etc. Through feature matching, the person in the local gallery that best matches the person in the picture is identified, and then the corresponding person information is obtained. This process can automatically prompt the user of the identities and relationships of the persons in the picture, effectively preventing the disclosure of information about family members or other important persons due to accidental sending of pictures, and protecting the privacy of the user's interpersonal relationships.

[0038] In an alternative embodiment of the present application, detail recognition includes: identifying the clothing and accessory items worn by the associated persons in the picture, and using the product information corresponding to the clothing and accessory items as detail information. Optionally, the product information includes at least one of the following: brand name, product model number, product selling price. In this embodiment, the clothing and accessory items worn by the persons in the picture, such as famous brand bags and limited edition shoes, are identified, and their product information is obtained. The system can prompt the user of this information that may disclose economic status or personal preferences. For example, a photo showing off wealth may attract criminals. This function helps the user to share personal information carefully. For the recognition of the clothing and accessory items worn by the associated persons in the picture and the acquisition of their product information, image recognition technology in computer vision, combined with deep learning algorithms, such as convolutional neural network (CNN) or region-based convolutional neural network (R-CNN) series algorithms, can be used. First, the clothing and accessory items in the image are located through an object detection algorithm, and then the features of the clothing and accessory items are extracted using an image recognition algorithm and compared with a preset product database, so as to identify product information such as the brand and model number of the clothing and accessory items. This process can also combine natural language processing (NLP) technology to extract key information such as the product selling price from the product information. Through this technology, the system can effectively identify and prompt the user of the clothing and accessory item information in the picture that may disclose economic status or personal preferences, helping the user to share personal information carefully.

[0039] Such as Figure 3As shown, it is a picture to be sent selected by the user. In response to the send operation triggered by the user, the system immediately identifies the face element 101, clothing element 102, pants element 103, shoe element 104, and text element 105 in the picture to be sent. In the identification of the face element 101, it is possible to identify whether the face element 101 is a person stored in the picture library and the corresponding person information of the person by combining the person information stored in the picture library. In the identification of clothing items such as the clothing element 102, pants element 103, and shoe element 104, product information such as clothing trademarks 106 and shoe trademarks 107 can be identified. In the identification of the text element 105, the license plate number XY1234 can be identified.

[0040] As Figure 4 As shown, it is another picture to be sent selected by the user. In response to the send operation triggered by the user, the system immediately identifies the face element 201, clothing element 202, and landmark building element 204 in the picture to be sent. In addition to being able to identify the person information corresponding to the face element 201 and the upper clothing trademark 203 corresponding to the clothing element 202, it is also possible to identify the landmark building element 204 in the background, thereby determining the picture location.

[0041] In an alternative embodiment of the present application, a sensitive information prompt list is generated according to the detailed information, including selecting at least one of the following as sensitive information and generating a sensitive information prompt list: Environmental scene; Location information; Text content; The person information corresponding to the target person among the associated persons who meets the first preset condition; The product information corresponding to the clothing item that meets the second preset condition.

[0042] It can be understood that not all details in the picture involve sensitivity. Therefore, it is necessary to screen the identified detailed information to generate a sensitive information prompt list, so as to effectively prompt the user.

[0043] Optionally, the first preset condition includes that the person relationship classification belongs to a preset classification. In this embodiment, the first preset condition is that the person relationship classification belongs to a preset classification, such as "family member". When it is identified that the associated person in the picture belongs to the family member category, their information is used as a sensitive information prompt. For example, if the user attempts to send a picture containing a family photo to a colleague, the system will prompt sensitive information. This function helps prevent the leakage of family information to inappropriate people and enhances privacy protection.

[0044] Optionally, the first preset condition includes that the classification of the relationship between people is different from the classification of the contact corresponding to the target contact. The first preset condition is that the classification of the relationship between people is different from the classification of the target contact. For example, if the user attempts to send a friend's photo to a family member, the system will give a prompt. This avoids misinformation being sent to people in different social circles and protects the privacy of the user's social relationships.

[0045] Optionally, the second preset condition includes the clothing and accessories worn by the target person. The second preset condition is the clothing and accessories worn by the target person. The system identifies and prompts specific items worn by the person in the picture, such as designer clothing. This helps the user realize the possible disclosure of economic status or personal preferences and avoid unnecessary showing off or risks.

[0046] Optionally, the second preset condition includes that the brand name belongs to a preset brand set. The second preset condition is that the brand name belongs to a preset brand set. The system identifies and prompts the preset high-value brands that appear in the picture, such as luxury brands. This helps the user share information about items that may pose security risks with caution.

[0047] Optionally, the second preset condition includes that the selling price of the commodity is greater than a preset price threshold. The second preset condition is that the selling price of the commodity is greater than a preset price threshold. The system identifies and prompts items of higher value in the picture, such as expensive electronic products. This helps the user avoid inadvertently disclosing financial information that may attract criminals and enhances their security awareness.

[0048] If the user triggers a "send operation" for the Figure 3 and Figure 4 pending picture shown, the system will, in response to this operation, identify each detail of the picture and generate a corresponding list of sensitive information prompts, such as Figure 5 shown. Six prompts are made for the person information, location information, text content, etc. in the picture to prevent the user from disclosing personal sensitive information to the other party.

[0049] In an optional embodiment of the present application, after step S2, the computer big data information security risk behavior recognition method further includes at least one of the following: In response to a selection operation for a target sensitive information in the list of sensitive information prompts, cancel the send operation of the target picture involving the target sensitive information. When the user selects a certain target sensitive information that they think may disclose privacy or sensitivity from the list of sensitive information prompts, the system can automatically cancel the send operation of the target picture containing this sensitive information to prevent information leakage and protect the user's privacy. As Figure 5 shown, if the user selects the second item "the picture was taken in Shanghai" in the list of sensitive information prompts, the system will cancel the send operation of the second pending picture.

[0050] In response to a selection operation on a target sensitive information in the sensitive information prompt list, desensitize the target picture involving the target sensitive information. When the user selects a certain target sensitive information in the sensitive information prompt list, the system can automatically desensitize the target picture containing the sensitive information, such as blurring, censoring, or cropping out the sensitive part, to ensure that the sensitive information has been removed before the picture is sent. For example Figure 6 As shown, in response to the user's selection operation on the second item "The shooting location of the picture is Shanghai" in the sensitive information prompt list, the system performs mosaic processing on the landmark building elements in the second picture to be sent. In an alternative embodiment of the present application, the desensitization processing includes at least one of the following: blurring the image element area corresponding to the target sensitive information; blurring the human element area corresponding to the target sensitive information; blurring the text element area corresponding to the target sensitive information.

[0051] In response to a confirmation operation, execute the sending instruction of at least one picture corresponding to the sending operation. After the user views the sensitive information prompt list and processes the pictures as needed (such as canceling the sending or desensitization processing), if the user confirms that there is no error and wishes to continue sending the remaining pictures, they can perform a confirmation operation, such as clicking the "Send" button. The system will then execute the sending operation and send the pictures that the user has confirmed to be correct to the target contact. This step ensures that the user can safely send pictures after fully understanding the picture content and making corresponding processing.

[0052] In a second aspect, the present application provides a computer big data information security risk behavior recognition system. The computer big data information security risk behavior recognition system includes one or more processors; one or more input devices, one or more output devices, and a memory. The above-mentioned processors, input devices, output devices, and memory are connected through a bus. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the memory. Among them, the processor is configured to call the program instructions to execute the operations of any method in the first aspect: It should be understood that in the embodiments of the present invention, the so-called processor may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0053] The input device may include a touchpad, a fingerprint sensor (for collecting the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device may include a display (such as an LCD), a speaker, etc.

[0054] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0055] In a specific implementation, the processor, input device, and output device described in the embodiments of the present invention may implement the implementation manners described in any method of the first aspect, or may also implement the implementation manner of the terminal device described in the embodiments of the present invention, which will not be elaborated herein.

[0056] In a third aspect, the present application also discloses an electronic device, including: A detail recognition module, configured to recognize detail information in at least one picture in response to a sending operation of sending at least one picture to a target contact; A sensitive information prompt list generation and display module, configured to generate a sensitive information prompt list according to the detail information and display the sensitive information prompt list; A sensitive information processing module, configured to perform at least one of the following: In response to a selection operation on a target sensitive information in the sensitive information prompt list, cancel the sending operation of a target picture involving the target sensitive information; In response to a selection operation on a target sensitive information in the sensitive information prompt list, perform desensitization processing on a target picture involving the target sensitive information; In response to a confirmation operation, execute the sending instruction of the at least one picture corresponding to the sending operation.

[0057] Fourthly, the present invention provides a computer-readable storage medium storing a computer program which includes program instructions. When the program instructions are executed by a processor, the steps of any method of the first aspect are implemented.

[0058] The above computer-readable storage medium may be an internal storage unit of the terminal device in any of the foregoing embodiments, such as the hard disk or memory of the terminal device. The above computer-readable storage medium may also be an external storage device of the above terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the above terminal device. Further, the above computer-readable storage medium may also include both the internal storage unit and the external storage device of the above terminal device. The above computer-readable storage medium is used to store the above computer program and other programs and data required by the above terminal device. The above computer-readable storage medium may also be used to temporarily store the data that has been output or is to be output.

[0059] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0060] In the several embodiments provided in the present application, it should be understood that the disclosed terminal device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices or units, and can also be in electrical, mechanical or other forms of connection.

[0061] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the objectives of the solution of the embodiments of the present invention.

[0062] In addition, in each embodiment of the present invention, the various functional units may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0063] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0064] In the various embodiments of the present disclosure, the expressions "first", "second", "the first", or "the second" can modify various components without regard to order and / or importance, but these expressions do not limit the corresponding components. The above expressions are only configured for the purpose of distinguishing elements from other elements. For example, a first user device and a second user device represent different user devices, although both are user devices. For example, without departing from the scope of the present disclosure, the first element can be referred to as the second element, and similarly, the second element can be referred to as the first element.

[0065] When an element (e.g., a first element) is referred to as being “(operatively or communicatively) coupled” or “(operatively or communicatively) coupled to” or “connected to” another element (e.g., a second element), it is to be understood that the one element is directly connected to the other element or the one element is indirectly connected to the other element via yet another element (e.g., a third element). Conversely, it is understood that when an element (e.g., a first element) is referred to as being “directly connected” or “directly coupled” to another element (a second element), no element (e.g., a third element) is inserted therebetween.

[0066] It should be noted that, in this text, the term “comprising”, “including” or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement “comprising a...” does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising such element. In addition, components, features, elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their interpretation in the specific embodiment or further in combination with the context of the specific embodiment.

[0067] The above description is only an optional embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, a technical solution formed by mutually replacing the above features with (but not limited to) technical features having similar functions disclosed in the present application.

[0068] Depending on the context, the words “if”, “when” as used herein may be interpreted as “when” or “while” or “in response to determining” or “in response to detecting”. Similarly, depending on the context, the phrase “if determined” or “if detected (stated condition or event)” may be interpreted as “when determined” or “in response to determining” or “when detected (stated condition or event)” or “in response to detecting (stated condition or event)”.

[0069] The above description is only an optional embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present application.

[0070] The above is only an optional embodiment of the present application and is not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for identifying computer big data information security risk behavior, characterized in that: The following steps are involved: S1, in response to a sending operation of sending at least one picture to a target contact, identifying detailed information in the at least one picture; S2, generating a sensitive information prompt list according to the detailed information, and displaying the sensitive information prompt list; After step S2, the computer big data information security risk behavior identification method further includes at least one of the following: In response to a selection operation on target sensitive information in the sensitive information prompt list, canceling a sending operation of a target image involving the target sensitive information; In response to a selection operation on target sensitive information in the sensitive information prompt list, desensitizing a target image related to the target sensitive information; In response to the confirmation operation, a sending instruction of the at least one picture corresponding to the sending operation is executed.

2. The computer big data information security risk behavior identification method according to claim 1 is characterized in that: The identifying the detail information in the at least one picture includes at least one of the following: Perform detail recognition on each picture to obtain the detail information in each picture; Performing detail recognition on the portrait image containing facial elements in the at least one image to obtain detail information of the portrait image; Detail recognition is performed on a text image containing text elements in the at least one image to obtain detail information of the text image.

3. The computer big data information security risk behavior identification method according to claim 2 is characterized in that: The detail identification includes at least one of the following: Identify the environment scene in the picture and use the environment scene as detail information; Identify landmark buildings in the background area of ​​the image, and use location information of the landmark buildings as detail information; Identify text elements in the image, understand text content corresponding to the text elements, and use the text content as detail information; Identify the associated person in the picture that matches the person information stored in the picture library, and use the person information corresponding to the associated person as the detail information; Identify clothing items worn by the associated person in the picture, and use commodity information corresponding to the clothing items as detail information.

4. The computer big data information security risk behavior identification method according to claim 3 is characterized in that: The character information includes at least one of the following: a character nickname stored in the gallery, a character relationship classification stored in the gallery; The product information includes at least one of the following: brand name, product model number, and product price.

5. The computer big data information security risk behavior identification method according to claim 4 is characterized in that: The generating a sensitive information prompt list according to the detailed information includes: Select at least one of the following as sensitive information and generate a sensitive information prompt list: The environmental scene; the location information; the text content; The character information corresponding to the target character who meets the first preset condition among the associated characters; The commodity information corresponding to the clothing item that meets the second preset condition.

6. The computer big data information security risk behavior identification method according to claim 5 is characterized in that: The first preset condition includes at least one of the following: The character relationship classification belongs to the preset classification; The character relationship classification is different from the contact classification corresponding to the target contact.

7. The computer big data information security risk behavior identification method according to claim 5 is characterized in that: The second preset condition includes at least one of the following: The clothing items worn by the target person; The brand name belongs to a preset brand set; The selling price of the product is greater than a preset price threshold.

8. The computer big data information security risk behavior identification method according to claim 1 is characterized in that: The desensitization treatment includes at least one of the following: Performing blur processing on the image element area corresponding to the target sensitive information; Performing blur processing on the character element area corresponding to the target sensitive information; The text element area corresponding to the target sensitive information is blurred.

9. A computer big data information security risk behavior identification system, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 8.

10. An electronic device, characterized in that: include: A detail recognition module, configured to recognize detail information in the at least one picture in response to a sending operation of sending at least one picture to a target contact; A sensitive information prompt list generation and display module, used to generate a sensitive information prompt list according to the detailed information, and display the sensitive information prompt list; A sensitive information processing module, configured to perform at least one of the following: In response to a selection operation on target sensitive information in the sensitive information prompt list, canceling a sending operation of a target image involving the target sensitive information; In response to a selection operation on target sensitive information in the sensitive information prompt list, desensitizing a target image related to the target sensitive information; In response to the confirmation operation, a sending instruction of the at least one picture corresponding to the sending operation is executed.

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