WO2021217899A1

WO2021217899A1 - Method, apparatus, and device for encrypting display information, and storage medium

Info

Publication number: WO2021217899A1
Application number: PCT/CN2020/102535
Authority: WO
Inventors: 温桂龙
Original assignee: 深圳壹账通智能科技有限公司
Priority date: 2020-04-30
Filing date: 2020-07-17
Publication date: 2021-11-04
Also published as: CN111552984A

Abstract

A method, apparatus, and device for encrypting display information, and a storage medium. The method comprises: when display information of a current device is detected, acquiring a target sensitivity level of the current display information, and when the target sensitivity level reaches a preset sensitivity level, collecting, on the basis of a first preset frequency, a first display region image of a target display region corresponding to the current device (S10); determining, on the basis of a preset camera device identification model and of the first display region image, whether a camera device is present in the target display region (S20); and insofar as a camera device is present in the target display region, encrypting the current device and/or the current display information (S30). In addition, also related to the blockchain technology, the camera device identification model can be stored in a blockchain.

Description

Encryption method, device, equipment and storage medium for display information

This application claims the priority of the Chinese patent application filed with the Chinese Patent Office on April 30, 2020, the application number is 202010362798.X, and the invention title is "Encryption method, device, equipment and storage medium for displaying information", and its entire content Incorporated in this application by reference.

Technical field

This application relates to the field of information security technology, and in particular to an encryption method, device, device, and computer-readable storage medium for displaying information.

Background technique

With the rapid development of network information technology, information security has attracted the attention of individuals, government agencies and enterprises. At present, in order to prevent the leakage of internal sensitive data, various methods such as isolating internal and external networks and restricting USB to prevent the transmission of data to prevent the leakage of sensitive information are generally used to prevent the leakage of sensitive information, thereby preventing the information on a specific computer from being transmitted through physical interfaces such as the Internet and USB. go outside. However, the inventor realized that it is still difficult to avoid information leakage caused by capturing the contents of a computer screen through a mobile phone camera or video camera. The main solution at present is to add a watermark to the computer screen to identify the screen content to restrict actions such as taking pictures. However, the method of preventing sensitive information leakage by adding watermarks on the screen cannot freely control the addition and removal of watermarks, so it will have a certain impact on the display effect of the screen content, which not only reduces the user experience, but also displays the effect of preventing information leakage. Poor. Therefore, how to solve the poor effect of preventing leakage of display information of the existing equipment has become a technical problem to be solved urgently at present.

Technical solutions

The main purpose of this application is to provide an encryption method, device, device, and computer-readable storage medium for displaying information, aiming to solve the technical problem of poor anti-leakage effects of existing devices on displaying information.

In order to achieve the above objective, the present application provides an encryption method for display information. The encryption method for display information includes the following steps:

When the current device display information is monitored, the target sensitivity level of the currently displayed information is acquired, and when the target sensitivity level reaches the preset sensitivity level, the display of the target display area corresponding to the current device is collected according to the first preset frequency The first image of the area;

Judging whether there is a camera device in the target display area according to a preset camera device recognition model and the first image of the display area;

When there is a camera device in the target display area, corresponding encryption processing is performed on the current device and/or the current display information.

In addition, in order to achieve the above objective, the present application also provides an encryption device for displaying information, and the encryption device for displaying information includes:

The first image acquisition module is used to acquire the target sensitivity level of the currently displayed information when the current device display information is monitored, and when the target sensitivity level reaches the preset sensitivity level, collect the current The first image of the display area of the target display area corresponding to the device;

An area device monitoring module, configured to determine whether there is a camera device in the target display area according to a preset camera device recognition model and the first image of the display area;

The display information encryption module is configured to perform corresponding encryption processing on the current device and/or current display information when there is a camera device in the target display area.

In addition, in order to achieve the above-mentioned object, the present application also provides an encryption device for displaying information. The encryption device for displaying information includes a processor, a memory, and display information that is stored on the memory and can be executed by the processor. The encryption program of the display information, when the encryption program of the display information is executed by the processor, the following steps are implemented:

In addition, in order to achieve the above-mentioned object, the present application also provides a computer-readable storage medium that stores an encryption program for displaying information, where the encryption program for displaying information is executed by a processor. The following steps:

The present application provides an encryption method for display information, by acquiring the target sensitivity level of the currently displayed information when the current device display information is monitored, and when the target sensitivity level reaches the preset sensitivity level, according to the first preset frequency Collect the first image of the display area of the target display area corresponding to the current device; determine whether there is a camera in the target display area according to a preset camera device recognition model and the first image of the display area; display on the target When there is a camera device in the area, corresponding encryption processing is performed on the current device and/or the current display information. Through the above method, when the device displays the display information of the target sensitivity level, the present application recognizes the display area image corresponding to the current device through the camera device recognition model, that is, the display area image is classified through the KNN algorithm and the preset image type for real-time detection Whether there is a camera device that causes display information leakage in the display area of the current device. As a result, according to whether there is a camera device in the display area of the device, dynamically control whether to encrypt the display information in the current device in real time, not only can effectively avoid information leakage caused by the camera device, but also improve the anti-leakage effect of the device display information , And avoid adding a watermark to all the displayed information of the device, which improves the user experience and solves the technical problem of poor anti-leakage effect of the existing device display information.

Description of the drawings

FIG. 1 is a schematic diagram of the hardware structure of an encryption device for displaying information involved in a solution of an embodiment of the application;

2 is a schematic flowchart of a first embodiment of a method for encrypting display information in this application;

FIG. 3 is a schematic flowchart of a second embodiment of a method for encrypting display information in this application;

4 is a schematic flowchart of a third embodiment of a method for encrypting display information in this application;

FIG. 5 is a schematic diagram of the functional modules of the first embodiment of the encryption device for displaying information in this application.

The realization, functional characteristics, and advantages of the purpose of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Embodiments of the present invention

It should be understood that the specific embodiments described here are only used to explain the present application, and are not used to limit the present application.

The encryption method for display information involved in the embodiments of the present application is mainly applied to an encryption device for displaying information. The encryption device for displaying information may be a device with display and processing functions such as a PC, a portable computer, and a mobile terminal.

Referring to FIG. 1, FIG. 1 is a schematic diagram of the hardware structure of the encryption device for displaying information involved in the solution of the embodiment of the application. In the embodiment of the present application, the encryption device for displaying information may include a processor 1001 (for example, a CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components; the user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard); the network interface 1004 may optionally include a standard wired interface, a wireless interface (Such as WI-FI interface); the memory 1005 can be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 can optionally also be a storage device independent of the aforementioned processor 1001 .

Those skilled in the art can understand that the hardware structure shown in FIG. 1 does not constitute a limitation on the encryption device for displaying information, and may include more or less components than those shown in the figure, or a combination of certain components, or different components. Layout.

Continuing to refer to FIG. 1, the memory 1005 as a computer-readable storage medium in FIG. 1 may include an operating system, a network communication module, and an encrypted program for displaying information.

In FIG. 1, the network communication module is mainly used to connect to the server and perform data communication with the server; and the processor 1001 can call the encryption program of the display information stored in the memory 1005, and execute the encryption method of the display information provided by the embodiment of the present application .

The embodiment of the present application provides an encryption method for display information.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of a first embodiment of a method for encrypting display information in this application.

In this embodiment, the encryption method of the displayed information includes the following steps:

Step S10: When the current device display information is monitored, the target sensitivity level of the currently displayed information is acquired, and when the target sensitivity level reaches a preset sensitivity level, the target display corresponding to the current device is collected according to the first preset frequency The first image of the display area of the area;

Information security is an important topic in the modern Internet. Whether it is in a specific government agency or various computer systems in various enterprises, there will be some specific information in the system that is relatively confidential information, and should not be It is transmitted to the external Internet environment. In order to achieve the purpose of confidentiality, the general method will prevent the leakage of sensitive information through various forms such as isolation of internal and external networks, restriction of USB and other forms of data transmission to the outside. Now it is basically possible to prevent the information on a specific computer from being transmitted to the outside through physical interfaces such as the Internet and USB. However, it is still difficult to avoid the information leakage caused by shooting the screen content through mobile phone cameras, video cameras and other devices. At present, there are two solutions, one is to limit the shooting; the other is to add a watermark on the screen, However, this method needs to always add a watermark to the screen content, which not only affects the user experience, but also has a poor information leakage prevention effect. This application takes into account that no matter what equipment is used for taking photos, videos, etc., there will be a process of aligning, focusing, and shooting the content to be shot. This takes a certain amount of time. During this time, the screen is recognized by artificial intelligence. Whether there is the display information of the screen shot by the camera equipment before, use this as a basis to determine whether the current screen content is at risk of being shot, and then perform operations such as warning, adding watermark, and canceling the display content according to the sensitivity level of the sensitive content. Effectively avoid the information leakage caused by taking pictures and videos through mobile phone cameras, video cameras, etc. In order to solve the above problems, this application uses the camera device recognition model to identify the display area image corresponding to the current device when the device displays the display information of the target sensitivity level. Detect whether there is a camera device that causes display information leakage in the display area of the current device. Thus, in real time, according to whether there is a camera device in the display area of the device, it is dynamically controlled whether to encrypt the display information in the current device. Specifically, the currently displayed information includes text information, picture information, or video information. In this embodiment, text information is taken as an example for description. The preset sensitivity level can be preset to "non-sensitive level", "general sensitivity level", and "high sensitivity level". The terminal can detect the sensitivity level of the current text content in real time or regularly. In a specific embodiment, multiple text content containing non-sensitive, general sensitive, and high sensitivity can be obtained, and multiple text content containing non-sensitive, generally sensitive, and highly sensitive can be used as the training set, and the training set can be input to the volume. The product neural network is trained to build a sensitivity level recognition model, and the sensitivity level of the current text content is detected through the sensitivity level recognition model. When it is detected that the current display information reaches the preset sensitivity level, the first image of the display area of the target display area corresponding to the current device is collected through the front camera of the terminal at the first preset frequency. The first preset frequency is set according to the user's own needs.

Further, the step S10 specifically includes:

When the current device display information is monitored, determine whether the current display information is text information;

When the currently displayed information is text information, extract keywords in the currently displayed information, and determine the target sensitivity level of the currently displayed information according to the keywords;

When the target sensitivity level reaches the preset sensitivity level, the first image of the display area of the target display area corresponding to the current device is acquired according to the first preset frequency.

In this embodiment, when the terminal detects the current text information, it extracts the keywords of the current text information and determines whether the keywords are preset storage keywords. If the keywords are preset storage keywords, the current text information is determined according to the keywords. Sensitivity level. If it is detected that the sensitivity level of the current text information belongs to the non-sensitive level in the preset sensitivity level, no operation is performed on the current text, that is, the current text content does not need to be protected; if the sensitivity level of the current text information is detected as normal When the sensitivity level is sensitive, the face information of the current user is obtained through the front camera of the terminal to detect whether the face information of the current user matches the face information of the preset storage user. If the current user is not the preset storage user, then Add a watermark containing the preset stored user information and the current user's information to the current text on the screen. If the current user is a preset storage user, add the current text on the screen with a watermark containing the preset stored user information; if the current user is detected When the text content belongs to the high sensitivity level among the preset sensitivity levels, the front camera of the terminal detects whether there is a camera device in the display area of the current text content.

Further, after the step S10, it further includes:

Perform defuzzification, grayscale or binarization processing on the first image of the display area, and perform image noise reduction processing on the processed first image of the display area through median filtering, average filtering or adaptive Wiener filtering .

In this embodiment, the terminal collects an image at a first preset frequency, preprocesses the image, and determines whether the preprocessed image has a camera device, and if there is a camera device, it determines whether the image has a human face. Among them, the preprocessing process is: deblurring the image, that is, using the Laplacian algorithm to identify the blurred image, and if the image is blurred, the detection of the image is abandoned. In addition, the image is also grayed out or binarized. The gray-scale processing is: use the RGB model to represent each pixel of the image, and take the average value of R, G, and B of each pixel to replace the original R, G, and B values to obtain the current image's grayscale Value; binarization processing is: divide the pixels of the image into two parts, black and white, black as foreground information, white as background information, in order to process the original image except for the target text other objects, Background etc. The terminal also performs image noise reduction on the pictures, that is, filtering by means of median filtering, mean filtering, adaptive Wiener filtering, etc., to deal with the image noise caused by the process of image acquisition, compression, and transmission.

Step S20: Determine whether there is a camera device in the target display area according to a preset camera device recognition model and the first image of the display area;

In this embodiment, the preprocessed image is input into the preset camera equipment recognition model. In a specific embodiment, the terminal pre-collects a large number of image data that exist in the terminal camera, video camera, or other types of camera equipment, and trains the above-mentioned image data as a training set to obtain the first preset camera equipment recognition model. That is, the K-nearest Neighbors (KNN) is used to classify the first image of the display area, and it is determined whether the first image of the display area has a camera device type image or does not have a camera device type image. Thus, it is determined whether there is an imaging device in the target display area.

Step S30, when there is a camera device in the target display area, perform corresponding encryption processing on the current device and/or current display information.

In this embodiment, when the terminal detects that there is a camera device in the target display area that is aimed at the current text content, it turns off the current device or cancels the display of the current text content on the screen; if the terminal detects that there is a camera device that is not aimed at the current text content If yes, a prompt message is sent to the user, that is, the prompt message is "Camera equipment detected, beware of leaking sensitive information". Thus, according to a preset encryption strategy, corresponding encryption processing is performed on the current device and/or current display information.

This embodiment provides an encryption method for display information, by acquiring the target sensitivity level of the currently displayed information when the current device display information is monitored, and when the target sensitivity level reaches the preset sensitivity level, according to the first preset Frequency collection of the first image of the display area of the target display area corresponding to the current device; according to a preset camera device recognition model and the first image of the display area, it is determined whether there is a camera device in the target display area; When there is a camera device in the display area, corresponding encryption processing is performed on the current device and/or the current display information. Through the above method, when the device displays the display information of the target sensitivity level, the present application recognizes the display area image corresponding to the current device through the camera device recognition model, that is, the display area image is classified through the KNN algorithm and the preset image type for real-time detection Whether there is a camera device that causes display information leakage in the display area of the current device. As a result, according to whether there is a camera device in the display area of the device, dynamically control whether to encrypt the display information in the current device in real time, not only can effectively avoid information leakage caused by the camera device, but also improve the anti-leakage effect of the device display information , And avoid adding a watermark to all the displayed information of the device, which improves the user experience and solves the technical problem of poor anti-leakage effect of the existing device display information.

Referring to FIG. 3, FIG. 3 is a schematic flowchart of a second embodiment of a method for encrypting display information in this application.

Based on the embodiment shown in FIG. 2 above, in this embodiment, the step S20 specifically includes:

Step S21, inputting the first image of the display area into the imaging device identification model, so as to output the classification result of the first image of the display area through the proximity algorithm KNN algorithm in the imaging device recognition model;

In this embodiment, the preprocessed image is input to the first preset camera device recognition model (the terminal collects a large amount of image data of the terminal camera, video camera, or other types of camera devices in advance, and uses the above image data as a training set for training Obtain the first preset camera equipment recognition model), that is, use KNN (K-nearest Neighbors) algorithm. Firstly, the principal components analysis (PCA) is used for the picture to convert the image information into a vector table, and then the Euclidean distance is selected as the distance calculation method, and the vector corresponding to each type of image point is calculated. The KNN algorithm is selected to select K points with the closest distance, and the distribution of K points in each type of image is determined. Among them, k=5 is adopted by default, and the final value of k is obtained by adjusting the value according to the effect of the training machine. Calculate the Euclidean distance between the target image point and each type of image, and determine which class it belongs to according to the k value, that is, output the classification result of "camera equipment present" or "camera equipment absent".

It should be emphasized that, in order to further ensure the privacy and security of the above-mentioned camera equipment design model, the above-mentioned camera equipment design model can also be stored in a node of a blockchain.

Further, the step S21 specifically includes:

The first image of the display area is input to the imaging device identification model, and the first image of the display area is converted into image vector data based on the principal component analysis dimensionality reduction algorithm PCA dimensionality reduction algorithm in the imaging device recognition model ；

Based on the Euclidean distance calculation formula and the image vector data, respectively calculate the Euclidean distance between the first image of the display area and the type image of the camera device and the type image of the camera device in the camera device recognition model;

Determine the classification result of the first image in the display area based on the Euclidean distance between the first image in the display area and each type of image and the KNN algorithm.

The specific construction steps of the camera device recognition model in this embodiment are as follows:

1. Pre-acquisition a large number of image data from mobile phone cameras, cameras or other types of camera equipment aimed at the computer screen, there are image data from mobile phone cameras, cameras or other types of camera equipment but not aimed at the computer screen, and other irrelevant image data .

2. Mark and classify the above-mentioned picture data into three types of pictures: "Camera equipment is present on the screen", "Camera equipment is present but not on the screen", "Camera equipment is not present", and the data is divided into training sets and Test set, use the training set to train the model, and the test set for testing.

3. First, perform a two-class classification, that is, into two categories: "with camera equipment" and "without camera equipment". KNN (K-nearest Neighbors) for classification, specifically:

(1), through the PCA dimensionality reduction algorithm to reduce the dimensionality of each image, and convert each image into a vector representation;

(2) Choose Euclidean distance as the distance calculation method to calculate the distance between the test data and each training data, d=sqrt(∑(x _i1 -xi ₂ ) ^{^2} );

In specific embodiments, commonly used methods for measuring distance include: Euclidean distance, cosine value (cos), correlation, Manhattan distance, or others.

(3). Calculate the training set data, T=(x ₁ ,y ₁ ),(x ₂ ,y ₂ ),...,(x _N ,y _N );

(4) Sort according to the increasing relationship of distance, select the K points with the smallest distance, determine the appearance frequency of the category of the first K points, and return the category with the highest appearance frequency among the first K points as the predicted classification of the test data.

Because, when the value of K is too small, once there are noise components, they will have a greater impact on the prediction. For example, when the K value is 1, once the nearest point is noise, then there will be a deviation, the K value The decrease of means that the overall model becomes complicated and is prone to overfitting; if the value of K is too large, it is equivalent to predicting with training examples in a larger neighborhood, and the approximate error of learning will increase . At this time, instances far away from the input target point will also play a role in the prediction, making the prediction wrong. The increase of the K value means that the overall model becomes simple; if K==N, then all instances are taken, that is, to take the most points under a certain category in the instances, which has no practical significance for prediction NS;

In this embodiment, the value of K should be an odd number as much as possible to ensure that more categories will be generated at the end of the calculation result. If an even number is selected, an equal situation may be generated, which is not conducive to prediction. Specifically, k=5 is adopted by default, and the final value of k is obtained by adjusting the value according to the effect of the training machine.

(5) Calculate the Euclidean distance of the target image, determine which class it belongs to according to the k value, and output the result.

y=argmaxc _j ∑x _i ∈Nk(x)I(y _i = c _j) ,i=1,2,...,N;

If the result of the second classifier in the above steps is no camera equipment, the output result is "no camera equipment", if it is "camera equipment", you need to distinguish between "aligned to the screen" and "non-aligned to the screen". At this time, it is only necessary to build another KNN classifier to perform two classifications, and all data can be divided into three categories: "camera equipment exists but is aimed at the screen", "camera equipment exists but not aimed at the screen", and "camera equipment does not exist".

In this embodiment, the first image of the display area is input to the imaging device identification model, and the PCA dimensionality reduction algorithm is the first image of the display area based on the principal component analysis dimensionality reduction algorithm in the imaging device recognition model. Converted into image vector data; and then based on the Euclidean distance calculation formula and the image vector data, the first image of the display area and the camera device recognition model in the first image and the camera device recognition model are respectively calculated for the type image of the camera device and the type of the camera device that does not exist The Euclidean distance between images; based on the trained K value in the trained camera equipment recognition model, select the K points closest to the first image in the display area, and count the picture types belonging to the K points, which will be the most The picture type determines the classification result of the first image in the display area.

Step S22: Determine whether there is an imaging device in the target display area according to the classification result of the first image in the display area.

In this embodiment, according to the image type to which the most image points belong, that is, "the camera device exists" or "the camera device does not exist", it is determined whether there is a camera device in the display area corresponding to the current device.

Referring to FIG. 4, FIG. 4 is a schematic flowchart of a third embodiment of a method for encrypting display information in this application.

Based on the embodiment shown in FIG. 3, in this embodiment, the step S30 specifically includes:

Step S31: When there is a camera device in the target display area, determine whether there is a face image in the first image of the display area according to a preset face recognition model;

Step S32: If the face image exists in the first image of the display area, collect a second image of the display area of the target display area according to a second preset frequency, wherein the second preset frequency is greater than all the images. The first preset frequency;

Step S33, judging whether the facial emotion in the second image of the display area is the target facial emotion according to the preset emotion recognition model;

Step S34, when the facial emotion in the second image of the display area is the target facial emotion, turn off the current display information of the current device to prevent leakage of display information.

In this embodiment, if there is a camera device in the first image of the display area, a face recognition model (that is, a face recognition model obtained by acquiring multiple face data for training) is used to determine whether there is a face in the image. If there is a human face, the terminal collects the second image of the display area at a second preset frequency, where the second preset frequency is greater than the first preset frequency. In this way, the terminal recognizes that there is a camera device in the image and there is a human face, which means that the current text content is at a high risk of leaking, and the image can be collected at a larger frequency, the recognition is more accurate, and the vigilance is increased. If it is determined that there is a human face in the second image of the display area, emotion recognition is performed on the human face to obtain the recognition result, and corresponding security measures are taken according to the recognition result. Among them, the emotion recognition model can be used to obtain the emotion recognition model by obtaining facial data containing multiple emotions. If the face in the image is a nervous emotion, it means that the current text content is very serious. Large risk of leakage, cancel the display of the current text content on the screen. In this way, more confidential measures can be taken to prevent information leakage.

Further, the step S30 specifically includes:

When the camera device exists in the target display area, judging whether the camera device is aligned with the current display information according to the camera device recognition model;

When the imaging device is aimed at the currently displayed information, the currently displayed information of the current device is turned off to prevent the leakage of the displayed information.

In this embodiment, it is determined by the first preset camera device recognition model that the current text content is displayed in the display area of the camera device. Specifically, if there is a camera device, the second preset camera device recognition model is used to monitor whether the camera device is The text content is aligned. In the same way, the second preset camera device recognition model is obtained by pre-collecting a large amount of screen-aligned image data and non-aligned computer screen image data for training. Among them, the image is input to the second preset camera device recognition model, and emotion recognition is performed on the image at the same time, and an output result is obtained, that is, "the camera device is aligned with the current text content" or "the camera device is present but not aligned. The result of the current text content.

In addition, the embodiment of the present application also provides an encryption device for displaying information.

Referring to FIG. 5, FIG. 5 is a schematic diagram of the functional modules of the first embodiment of the encryption device for displaying information of this application.

In this embodiment, the encryption device for displaying information includes:

The first image acquisition module 10 is configured to acquire the target sensitivity level of the currently displayed information when the current device display information is monitored, and when the target sensitivity level reaches a preset sensitivity level, collect the The first image of the display area of the target display area corresponding to the current device;

The area equipment monitoring module 20 is configured to determine whether there is a camera device in the target display area according to a preset camera equipment recognition model and the first image of the display area;

The display information encryption module 30 is configured to perform corresponding encryption processing on the current device and/or current display information when there is a camera device in the target display area.

Further, the regional equipment monitoring module 20 specifically includes:

An image classification unit, configured to input the first image of the display area into the imaging device identification model to output the classification result of the first image of the display area through the proximity algorithm KNN algorithm in the imaging device recognition model ；

The device judging unit is configured to judge whether there is an imaging device in the target display area according to the classification result of the first image in the display area.

Further, the image classification unit is also used for:

Further, the encryption device for displaying information further includes an image preprocessing module, and the image preprocessing module is used for:

Further, the display information encryption module 30 is also used for:

When there is a camera device in the target display area, judging whether there is a face image in the first image of the display area according to a preset face recognition model;

If the face image exists in the first image of the display area, a second image of the display area of the target display area is collected according to a second preset frequency, where the second preset frequency is greater than the first Preset frequency

Judging whether the facial emotion in the second image of the display area is the target facial emotion according to a preset emotion recognition model;

When the facial emotion in the second image of the display area is the target facial emotion, the current display information of the current device is turned off to prevent the leakage of the display information.

Further, the display information encryption module 30 is also used for:

Further, the first image acquisition module 10 is also used for:

Among them, each module in the encryption device for displaying information corresponds to each step in the embodiment of the encryption method for displaying information, and its functions and implementation processes are not repeated here.

In addition, the embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium may be non-volatile or volatile.

The computer-readable storage medium of the present application stores an encryption program for display information, wherein when the encryption program for display information is executed by a processor, the steps of the encryption method for display information as described above are implemented.

Among them, the method implemented when the encryption program of the display information is executed can refer to the various embodiments of the encryption method of the display information of this application, which will not be repeated here.

The blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

It should be noted that in this article, the terms "include", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, It also includes other elements not explicitly listed, or elements inherent to the process, method, article, or system. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, article, or system that includes the element.

The serial numbers of the foregoing embodiments of the present application are for description only, and do not represent the superiority or inferiority of the embodiments.

Through the description of the above implementation manners, those skilled in the art can clearly understand that the above-mentioned embodiment method can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is better.的实施方式。 Based on this understanding, the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM) as described above. , Magnetic disks, optical disks), including several instructions to make a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.

The above are only the preferred embodiments of the application, and do not limit the scope of the patent for this application. Any equivalent structure or equivalent process transformation made using the content of the description and drawings of the application, or directly or indirectly applied to other related technical fields , The same reason is included in the scope of patent protection of this application.

Claims

An encryption method for display information, wherein the encryption method for display information includes the following steps:

When the current device display information is monitored, the target sensitivity level of the currently displayed information is acquired, and when the target sensitivity level reaches the preset sensitivity level, the display of the target display area corresponding to the current device is collected according to the first preset frequency The first image of the area;

Judging whether there is a camera device in the target display area according to a preset camera device recognition model and the first image of the display area;

When there is a camera device in the target display area, corresponding encryption processing is performed on the current device and/or the current display information.
The method for encrypting display information according to claim 1, wherein the step of judging whether there is a camera device in the target display area according to a preset camera device recognition model and the first image of the display area specifically comprises:

The first image of the display area is input to the camera device recognition model to output the classification result of the first image of the display area through the proximity algorithm KNN algorithm in the camera device recognition model, and the camera device recognition model Stored in the blockchain;

According to the classification result of the first image in the display area, it is determined whether there is an imaging device in the target display area.
The method for encrypting display information according to claim 2, wherein said inputting said first image of said display area into said imaging device identification model, and identifying the proximity algorithm in the model by said imaging device using KNN algorithm, The step of outputting the classification result of the first image in the display area specifically includes:

The first image of the display area is input to the imaging device identification model, and the first image of the display area is converted into image vector data based on the principal component analysis dimensionality reduction algorithm PCA dimensionality reduction algorithm in the imaging device recognition model ；

Based on the Euclidean distance calculation formula and the image vector data, respectively calculate the Euclidean distance between the first image of the display area and the type image of the camera device and the type image of the camera device in the camera device recognition model;

Determine the classification result of the first image in the display area based on the Euclidean distance between the first image in the display area and each type of image and the KNN algorithm.
The method for encrypting display information according to claim 1, wherein when the current device display information is monitored, the target sensitivity level of the current display information is acquired, and when the target sensitivity level reaches a preset sensitivity level, After the step of collecting the first image of the display area of the target display area corresponding to the current device at the first preset frequency, the method further includes:

Perform defuzzification, grayscale or binarization processing on the first image of the display area, and perform image noise reduction processing on the processed first image of the display area through median filtering, average filtering or adaptive Wiener filtering .
The method for encrypting display information according to claim 1, wherein the step of performing corresponding encryption processing on the current device and/or current display information when there is a camera device in the target display area specifically comprises:

When there is a camera device in the target display area, judging whether there is a face image in the first image of the display area according to a preset face recognition model;

If the face image exists in the first image of the display area, a second image of the display area of the target display area is collected according to a second preset frequency, where the second preset frequency is greater than the first Preset frequency

Judging whether the facial emotion in the second image of the display area is the target facial emotion according to a preset emotion recognition model;

When the facial emotion in the second image of the display area is the target facial emotion, the current display information of the current device is turned off to prevent the leakage of the display information.
The method for encrypting display information according to claim 1, wherein the step of performing corresponding encryption processing on the current device and/or current display information when there is a camera device in the target display area specifically comprises:

When the camera device exists in the target display area, judging whether the camera device is aligned with the current display information according to the camera device recognition model;

When the imaging device is aimed at the currently displayed information, the currently displayed information of the current device is turned off to prevent the leakage of the displayed information.
The method for encrypting display information according to any one of claims 1 to 6, wherein when the current device display information is monitored, the target sensitivity level of the current display information is obtained, and the target sensitivity level reaches a preset When the sensitivity level is sensitive, the step of collecting the first image of the display area of the target display area corresponding to the current device according to the first preset frequency specifically includes:

When the current device display information is monitored, determine whether the current display information is text information;

When the currently displayed information is text information, extract keywords in the currently displayed information, and determine the target sensitivity level of the currently displayed information according to the keywords;

When the target sensitivity level reaches the preset sensitivity level, the first image of the display area of the target display area corresponding to the current device is acquired according to the first preset frequency.
An encryption device for displaying information, wherein the encryption device for displaying information includes:

The first image acquisition module is used to acquire the target sensitivity level of the currently displayed information when the current device display information is monitored, and when the target sensitivity level reaches the preset sensitivity level, collect the current The first image of the display area of the target display area corresponding to the device;

An area device monitoring module, configured to determine whether there is a camera device in the target display area according to a preset camera device recognition model and the first image of the display area;

The display information encryption module is configured to perform corresponding encryption processing on the current device and/or current display information when there is a camera device in the target display area.
An encryption device for displaying information, wherein the encryption device for displaying information includes a processor, a memory, and an encryption program for displaying information stored on the memory and executable by the processor, wherein the displaying information When the encryption program of is executed by the processor, the following steps are implemented:

When the current device display information is monitored, the target sensitivity level of the currently displayed information is acquired, and when the target sensitivity level reaches the preset sensitivity level, the display of the target display area corresponding to the current device is collected according to the first preset frequency The first image of the area;

Judging whether there is a camera device in the target display area according to a preset camera device recognition model and the first image of the display area;

When there is a camera device in the target display area, corresponding encryption processing is performed on the current device and/or the current display information.
The encryption device for displaying information according to claim 9, wherein the step of judging whether there is a camera in the target display area according to a preset camera device recognition model and the first image of the display area specifically comprises:

The first image of the display area is input to the camera device recognition model to output the classification result of the first image of the display area through the proximity algorithm KNN algorithm in the camera device recognition model, and the camera device recognition model Stored in the blockchain;

According to the classification result of the first image in the display area, it is determined whether there is an imaging device in the target display area.
The encryption device for displaying information according to claim 10, wherein said inputting said first image of said display area into said imaging device identification model, and identifying the proximity algorithm in the model by said imaging device KNN algorithm, The step of outputting the classification result of the first image in the display area specifically includes:

The first image of the display area is input to the imaging device identification model, and the first image of the display area is converted into image vector data based on the principal component analysis dimensionality reduction algorithm PCA dimensionality reduction algorithm in the imaging device recognition model ；

Based on the Euclidean distance calculation formula and the image vector data, respectively calculate the Euclidean distance between the first image of the display area and the type image of the camera device and the type image of the camera device in the camera device recognition model;

Determine the classification result of the first image in the display area based on the Euclidean distance between the first image in the display area and each type of image and the KNN algorithm.
The encryption device for displaying information according to claim 9, wherein when the current device display information is monitored, the target sensitivity level of the currently displayed information is obtained, and when the target sensitivity level reaches a preset sensitivity level, After the step of collecting the first image of the display area of the target display area corresponding to the current device at the first preset frequency, the method further includes:

Perform defuzzification, grayscale or binarization processing on the first image of the display area, and perform image noise reduction processing on the processed first image of the display area through median filtering, average filtering or adaptive Wiener filtering .
The encryption device for displaying information according to claim 9, wherein the step of performing corresponding encryption processing on the current device and/or current display information when there is a camera device in the target display area specifically comprises:

When there is a camera device in the target display area, judging whether there is a face image in the first image of the display area according to a preset face recognition model;

If the face image exists in the first image of the display area, a second image of the display area of the target display area is collected according to a second preset frequency, where the second preset frequency is greater than the first Preset frequency

Judging whether the facial emotion in the second image of the display area is the target facial emotion according to a preset emotion recognition model;

When the facial emotion in the second image of the display area is the target facial emotion, the current display information of the current device is turned off to prevent the leakage of the display information.
The encryption device for displaying information according to claim 9, wherein the step of performing corresponding encryption processing on the current device and/or current display information when there is a camera device in the target display area specifically comprises:

When the camera device exists in the target display area, judging whether the camera device is aligned with the current display information according to the camera device recognition model;

When the imaging device is aimed at the currently displayed information, the currently displayed information of the current device is turned off to prevent the leakage of the displayed information.
The encryption device for displaying information according to any one of claims 9 to 14, wherein when the information displayed by the current device is monitored, the target sensitivity level of the currently displayed information is acquired, and the target sensitivity level reaches a preset When the sensitivity level is sensitive, the step of collecting the first image of the display area of the target display area corresponding to the current device according to the first preset frequency specifically includes:

When the current device display information is monitored, determine whether the current display information is text information;

When the currently displayed information is text information, extract keywords in the currently displayed information, and determine the target sensitivity level of the currently displayed information according to the keywords;

When the target sensitivity level reaches the preset sensitivity level, the first image of the display area of the target display area corresponding to the current device is acquired according to the first preset frequency.
A computer-readable storage medium, wherein an encrypted program for displaying information is stored on the computer-readable storage medium, and when the encrypted program for displaying information is executed by a processor, the following steps are implemented:

When the current device display information is monitored, the target sensitivity level of the currently displayed information is acquired, and when the target sensitivity level reaches the preset sensitivity level, the display of the target display area corresponding to the current device is collected according to the first preset frequency The first image of the area;

Judging whether there is a camera device in the target display area according to a preset camera device recognition model and the first image of the display area;

When there is a camera device in the target display area, corresponding encryption processing is performed on the current device and/or the current display information.
16. The computer-readable storage medium of claim 16, wherein the step of determining whether there is a camera in the target display area according to a preset camera device recognition model and the first image of the display area specifically comprises:

The first image of the display area is input to the camera device recognition model to output the classification result of the first image of the display area through the proximity algorithm KNN algorithm in the camera device recognition model, and the camera device recognition model Stored in the blockchain;

According to the classification result of the first image in the display area, it is determined whether there is an imaging device in the target display area.
17. The computer-readable storage medium according to claim 17, wherein said inputting said first image of said display area into said imaging device identification model, and recognizing a KNN algorithm in the model by said imaging device, The step of outputting the classification result of the first image in the display area specifically includes:

The first image of the display area is input to the imaging device identification model, and the first image of the display area is converted into image vector data based on the principal component analysis dimensionality reduction algorithm PCA dimensionality reduction algorithm in the imaging device recognition model ；

Based on the Euclidean distance calculation formula and the image vector data, respectively calculate the Euclidean distance between the first image of the display area and the type image of the camera device and the type image of the camera device in the camera device recognition model;

Determine the classification result of the first image in the display area based on the Euclidean distance between the first image in the display area and each type of image and the KNN algorithm.
The computer-readable storage medium according to claim 16, wherein when the current device display information is monitored, the target sensitivity level of the currently displayed information is acquired, and when the target sensitivity level reaches a preset sensitivity level, according to After the step of collecting the first image of the display area of the target display area corresponding to the current device at the first preset frequency, the method further includes:

Perform defuzzification, grayscale or binarization processing on the first image of the display area, and perform image noise reduction processing on the processed first image of the display area through median filtering, average filtering or adaptive Wiener filtering .
16. The computer-readable storage medium according to claim 16, wherein the step of performing corresponding encryption processing on the current device and/or current display information when there is an imaging device in the target display area specifically comprises:

When there is a camera device in the target display area, judging whether there is a face image in the first image of the display area according to a preset face recognition model;

If the face image exists in the first image of the display area, a second image of the display area of the target display area is collected according to a second preset frequency, where the second preset frequency is greater than the first Preset frequency

Judging whether the facial emotion in the second image of the display area is the target facial emotion according to a preset emotion recognition model;

When the facial emotion in the second image of the display area is the target facial emotion, the current display information of the current device is turned off to prevent the leakage of the display information.