A web screenshot monitoring method, device, equipment and product

Abstract

The application discloses a kind of Web screenshot monitoring method, device, equipment and product, belong to data privacy technical field, including judging whether onBlur event triggers, if yes, the content of clipboard is obtained, judge whether there is picture in the content of clipboard;If there is picture, based on the decoding algorithm of dark watermark, judge whether there is dark watermark in picture, if there is dark watermark, using OCR identifies dark watermark, obtains identification information;Decryption is carried out to identification information, obtains decryption information, matches decryption information with original mark information, if match is successful, then trigger reporting mechanism, original mark information is the dark watermark mark information stored in enterprise database.The application is used by cooperating with the decoding algorithm of dark watermark and OCR technology, identification information and original mark information are matched, to judge whether user carries out screen capture operation, improve work efficiency, enhance the security of data, while ensuring the effective traceability and management of important information.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of data privacy, and particularly relates to a Web screenshot monitoring method, device, equipment and product. BACKGROUND

[0002] In recent years, with the development of Internet technology, screenshot sharing and copy-paste operations have become popular, but at the same time, the problem of privacy leakage has also arisen. Malicious screenshots of users can cause intellectual property leakage, damage to brand image, leakage of business secrets, and competitive disadvantage of the product, etc. Among them, (1) intellectual property leakage: if the product of an enterprise contains unique design, unpublished function or sensitive information, if the user shares these contents through screenshots, it may lead to leakage of enterprise intellectual property, affect market competitiveness, and may also cause legal disputes; (2) damage to brand image: if the screenshots without authorization of the enterprise are maliciously edited and spread, it may mislead the public and damage the brand image and reputation of the enterprise; (3) leakage of business secrets: for some products that provide customized services or handle sensitive customer data, malicious screenshots of users may lead to leakage of business secrets or customer privacy information, causing direct economic losses to the enterprise and loss of customer trust; (4) competitive disadvantage: competitors of an enterprise can obtain information such as interface design or function details of the product by collecting screenshots, so as to make imitation or improvement.

[0003] The existing operation for preventing malicious screenshots of users mainly adopts screen watermarking or prohibits screenshot function, but the above operation will result in low user experience and low work efficiency. Therefore, based on the above problems, how to provide an effective technical solution to solve the problem of low user experience and low work efficiency in the prior art has become a difficult problem to be solved in the prior art. SUMMARY

[0004] The purpose of the present application is to provide a Web screenshot monitoring method, device, equipment and product to solve the above problems existing in the prior art.

[0005] In order to achieve the above purpose, the present application adopts the following technical solution:

[0006] In a first aspect, the present application provides a Web screenshot monitoring method, comprising:

[0007] determining whether an onBlur event is triggered, if so, obtaining the content of the clipboard, and determining whether there is a picture in the content of the clipboard;

[0008] if there is a picture, determining whether there is a watermark in the picture based on a watermark decoding algorithm, if there is a watermark, using OCR to recognize the watermark to obtain recognition information;

[0009] Decrypt the identification information to obtain decrypted information, match the decrypted information with original mark information, if the matching is successful, trigger the reporting mechanism, and the original mark information is the watermark mark information stored in the enterprise database.

[0010] In a possible design, based on a watermark decoding algorithm, it is determined whether a watermark exists in the picture, including:

[0011] Converting the picture from an RGB color space to a YCbCr color space to obtain a converted picture;

[0012] Channel extraction is performed on the converted picture to obtain a color difference channel, and the color difference channel includes a Cb channel and a Cr channel;

[0013] The color difference channel is amplified using a color difference amplification algorithm to obtain a picture after color difference amplification;

[0014] It is determined whether the picture after color difference amplification exists a watermark using a watermark decoding algorithm.

[0015] In a possible design, the watermark is identified using OCR to obtain identification information, including:

[0016] The watermark is segmented using an SSD algorithm to obtain segmented character blocks;

[0017] The segmented character blocks are feature extracted using a convolutional neural network to obtain character features;

[0018] The character features are identified using OCR to obtain identification information.

[0019] In a possible design, the convolutional neural network includes a convolutional layer, an activation layer, a pooling layer, and a fully connected layer, and the segmented character blocks are feature extracted using the convolutional neural network to obtain character features, including:

[0020] The convolutional layer extracts local character features from the segmented character blocks to obtain local character features;

[0021] The activation layer uses an activation function to perform nonlinear transformation on the local character features to obtain transformed local character features;

[0022] The pooling layer performs dimensionality reduction on the transformed local character features to obtain reduced local character features;

[0023] The fully connected layer combines the reduced local character features to obtain character features.

[0024] In a possible design, further including:

[0025] Obtain a protection page, generate a watermark according to original mark information and a preset generation rule, encrypt the watermark using an encryption algorithm, and obtain an encrypted watermark;

[0026] Embed the encrypted watermark in the protection page, and dynamically adjust the density of the encrypted watermark in the protection page using a watermark density adjustment algorithm based on user behavior.

[0027] In a possible design, dynamically adjusting the density of the encrypted watermark in the protection page using the watermark density adjustment algorithm based on user behavior includes:

[0028] Extract tags, elements and attributes of the protection page, and perform importance sorting on the tags, elements and attributes of the protection page according to a preset importance rule to obtain an importance sequence;

[0029] Preliminarily adjust the density of the encrypted watermark in the protection page according to the importance sequence;

[0030] Listen to a page event of a user, where the page event of the user includes time of mouse staying of the user, frequency of clicking of the user, and moving track of the mouse of the user;

[0031] Dynamically adjust the density of the encrypted watermark in the protection page using the watermark density adjustment algorithm based on user behavior according to the page event of the user.

[0032] In a possible design, generating a watermark according to original mark information and a preset generation rule, and encrypting the watermark using an encryption algorithm to obtain an encrypted watermark includes:

[0033] Obtain original mark information, and generate a watermark and a timestamp corresponding to the watermark according to the original mark information and a preset generation rule;

[0034] Generate a random private key using a random number generator;

[0035] Encrypt the watermark using an encryption algorithm according to the timestamp corresponding to the watermark and the random private key to obtain an encrypted watermark.

[0036] In a possible design, before the picture is parsed using the watermark decoding algorithm, the image is further subjected to a denoising and image enhancement operation.

[0037] In a second aspect, the present application provides a Web screenshot monitoring device for implementing the Web screenshot monitoring method in the first aspect, comprising:

[0038] A picture judgment module is configured to judge whether an onBlur event is triggered, and if so, obtain the content of the clipboard and judge whether the content of the clipboard contains a picture.

[0039] The watermark recognition module is configured to, if there is a picture, judge whether there is a watermark in the picture based on a watermark decoding algorithm, if there is a watermark, use OCR to recognize the watermark, and obtain recognition information.

[0040] The watermark matching module is configured to decrypt the recognition information to obtain decrypted information, match the decrypted information with original marking information, and if the matching is successful, trigger a reporting mechanism, wherein the original marking information is watermark marking information stored in an enterprise database.

[0041] In a third aspect, the present application provides a computer device, comprising a memory, a processor and a transceiver connected in sequence, wherein the memory is configured to store a computer program, the transceiver is configured to receive and send messages, and the processor is configured to read the computer program and execute the Web screenshot monitoring method according to any one of the above.

[0042] In a fourth aspect, the present application provides a computer program product comprising instructions, which, when executed on a computer, cause the computer to execute the Web screenshot monitoring method according to any one of the above.

[0043] The beneficial effects of the present application are as follows:

[0044] The present application discloses a Web screenshot monitoring method, device, equipment and product, comprising judging whether an onBlur event is triggered, if yes, obtaining the content of a clipboard, judging whether there is a picture in the content of the clipboard, if yes, judging whether there is a watermark in the picture based on a watermark decoding algorithm, if yes, using OCR to recognize the watermark, obtaining recognition information, decrypting the recognition information to obtain decrypted information, using the decrypted information to match original marking information, and if the matching is successful, triggering a reporting mechanism, wherein the original marking information is watermark marking information stored in an enterprise database. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 A flow chart of the Web screenshot monitoring method according to the first aspect of the embodiment is provided.

[0046] Figure 2 A module diagram of the Web screenshot monitoring device according to the second aspect of the embodiment is provided. DETAILED DESCRIPTION

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the present application will be briefly introduced below in connection with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the drawings is only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings. It should be noted that the description of these embodiments is used to help understand the present application, but does not constitute a limitation on the present application.

[0048] It should be understood that for the term "and / or" that may appear in the present text, it only describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, B alone, and A and B together; for the term " / and" that may appear in the present text, it describes another association object relationship, which means that there can be two relationships, for example, A / and B, which means that there are two cases of A alone and A and B together; in addition, for the character " / " that may appear in the present text, it generally means that the associated objects before and after are an "or" relationship.

[0049] Embodiments:

[0050] As shown in Figure 1 The first aspect of the present embodiment provides a Web (network) screenshot monitoring method, including but not limited to the following steps:

[0051] S1. Determine whether the onBlur event is triggered, if so, get the content of the clipboard, and determine whether there is an image in the content of the clipboard;

[0052] Specifically, the onBlur event is an event that occurs when an object loses focus, which is usually triggered when a form is validated, a page is optimized, or an automatic save is performed.

[0053] S2. If there is an image, determine whether there is a watermark in the image based on a watermark decoding algorithm, if there is a watermark, use OCR (Optical Character Recognition) to identify the watermark to obtain identification information;

[0054] Specifically, in step S2, based on the watermark decoding algorithm, it is determined whether there is a watermark in the image, including:

[0055] S21. Convert the image from RGB color space to YCbCr color space to obtain a converted image;

[0056] RGB color space is based on three basic colors of R (Red), G (Green) and B (Blue), and different degrees of superposition to produce a rich color; YCbCr color space Y is the brightness of color, Cb and Cr are the concentration offset of blue and red.

[0057] S22. Channel extraction is performed on the converted picture to obtain a color difference channel, which includes a Cb channel and a Cr channel;

[0058] S23. The color difference channel is amplified using a color difference amplification algorithm to obtain a picture after color difference amplification;

[0059] In this embodiment, the color difference amplification algorithm is a prior art, which can use linear stretching, histogram equalization or adaptive histogram equalization. The principle of linear stretching is to expand the color difference range of the image through linear transformation. Specifically, it maps each color value in the image to a new range, thereby increasing the contrast of the color; the principle of histogram equalization is to redistribute the pixel values of the image so that each color value appears approximately the same frequency in the image, thereby improving the global contrast of the image; the principle of adaptive histogram equalization is to equalize in local regions of the image, thereby improving the contrast while avoiding excessive noise enhancement, which will not be described in detail here.

[0060] S24. A watermark decoding algorithm is used to determine whether the picture after color difference amplification contains a watermark.

[0061] In this embodiment, the watermark decoding algorithm uses a transform domain-based algorithm, the principle of which is: embedding process: use Fourier transform or discrete cosine transform to convert the image from spatial domain to frequency domain, and embed watermark information in high or medium frequency sparseness; extraction process: inverse transform the image containing the watermark to extract the watermark information from the frequency domain coefficients.

[0062] Specifically, in step S2, the second watermark is recognized using OCR to obtain recognition information, including:

[0063] S25. The second watermark is segmented using an SSD algorithm (Single Shot MultiBox Detector, target detection algorithm) to obtain segmented character blocks;

[0064] S26. A convolutional neural network is used to extract features from the segmented character blocks to obtain character features;

[0065] The convolutional neural network includes a convolutional layer, an activation layer, a pooling layer and a fully connected layer. In step S26, the convolutional neural network is used to extract features from the segmented character blocks to obtain character features, including:

[0066] S261. The convolutional layer performs local feature extraction on the segmented character block to obtain local character features;

[0067] S262. The activation layer performs nonlinear transformation on the local character features using an activation function to obtain transformed local character features.

[0068] S263. The pooling layer performs dimension reduction on the transformed local character features to obtain reduced dimension local character features.

[0069] S264. The fully connected layer combines the reduced dimension local character features to obtain character features.

[0070] Further, before using the steganographic decoding algorithm to analyze the picture, it also includes denoising and image enhancement operations on the image.

[0071] In this embodiment, the denoising and image enhancement operations are performed to reduce the influence of noise and enhance the image features, which facilitates subsequent identification and matching of the steganographic image.

[0072] S27. Use OCR to identify character features to obtain recognition information.

[0073] S3. Decrypt the recognition information to obtain decrypted information, and match the decrypted information with the original marking information. If the matching is successful, trigger the reporting mechanism.

[0074] In one possible design, the reporting mechanism includes reporting suspected screenshot behavior and recording it in the log for further measures. After matching is successful, it also includes using the early warning system to warn the suspected screenshot behavior and remind the enterprise of the risk of data leakage.

[0075] In one possible design, it also includes obtaining a protection page, generating a steganographic watermark according to the original marking information and a preset generation rule, encrypting the steganographic watermark using an encryption algorithm to obtain an encrypted steganographic watermark.

[0076] Embed the encrypted steganographic watermark in the protection page and dynamically adjust the density of the encrypted steganographic watermark in the protection page using a watermark density adjustment algorithm based on user behavior.

[0077] Wherein, the original marking information is the steganographic watermark marking information stored in the enterprise database, and the steganographic watermark marking information includes but is not limited to enterprise name and product information. The preset generation rule is the steganographic watermark rule set by the user, which can be exemplified as text format and / or watermark form, etc.

[0078] Specifically, the watermark density adjustment algorithm based on user behavior is used to dynamically adjust the density of the encrypted steganographic watermark in the protection page, including:

[0079] Extract the tags, elements and attributes of the protection page, and sort the tags, elements and attributes of the protection page according to the preset importance rules to obtain an importance sequence;

[0080] According to the importance sequence, the density of the encrypted steganographic watermark in the protection page is preliminarily adjusted;

[0081] Listen to the user's page event, the user's page event includes the time of the user's mouse stay, the frequency of the user's click and the moving track of the user's mouse;

[0082] According to the user's page event, the density of the encrypted steganographic watermark in the protection page is dynamically adjusted using a watermark density adjustment algorithm based on user behavior.

[0083] Among them, the preset importance rule is the rule of the importance degree and the arrangement order between the label importance, the element importance and the attribute importance set by the user.

[0084] In the above disclosure, the density of the encrypted steganographic watermark in the page is adjusted using a watermark density adjustment algorithm based on user behavior, the importance of the region is judged according to the user behavior, if the importance of the region is higher, the density of the encrypted steganographic watermark in the region is increased, if the importance of the region is lower, the density of the encrypted steganographic watermark in the region is reduced. By adjusting the density distribution of the steganographic watermark, it is convenient to identify the steganographic watermark through the steganographic watermark decoding algorithm and OCR.

[0085] Further, according to the original mark information and the preset generation rule, the steganographic watermark is generated, and the steganographic watermark is encrypted using an encryption algorithm to obtain an encrypted steganographic watermark, including:

[0086] Obtain the original mark information, generate the steganographic watermark and the timestamp corresponding to the steganographic watermark according to the original mark information and the preset generation rule;

[0087] Using a random number generator, a random private key is generated;

[0088] According to the timestamp corresponding to the steganographic watermark and the random private key, the steganographic watermark is encrypted using an encryption algorithm to obtain an encrypted steganographic watermark.

[0089] The embodiment provides a Web screenshot monitoring method, which has the following beneficial effects:

[0090] (1) Enhance data security: indirect restriction of screenshot behavior by technical means can reduce the risk of information leakage and provide a safer service environment for users;

[0091] (2) Data-driven decision making: through listening and analyzing user behavior, enterprises can gain important insights on how to optimize products and services;

[0092] (3) Prevent potential threats: Early warning systems can help businesses quickly respond to any suspicious activity, reducing the likelihood of risk;

[0093] (4) Improve work efficiency: Using it within the enterprise can also help manage employee behavior, especially in the case of the increasing popularity of remote work to ensure efficient execution of work processes.

[0094] As Figure 2 shown, the second aspect of the embodiment provides a Web screenshot monitoring device, comprising:

[0095] a picture judgment module for judging whether the onBlur event is triggered, if so, obtaining the content of the clipboard, and judging whether there is a picture in the content of the clipboard;

[0096] a watermark recognition module for judging whether there is a dark watermark in the picture based on a dark watermark decoding algorithm if there is a picture, and using OCR to recognize the dark watermark to obtain recognition information if there is a dark watermark;

[0097] a watermark matching module for decrypting the recognition information to obtain decrypted information, matching the decrypted information with original marking information, and triggering a reporting mechanism if the matching is successful, wherein the original marking information is dark watermark marking information stored in an enterprise database.

[0098] The working process, working details and technical effects of the aforementioned device provided by the second aspect of the embodiment can be referred to the Web screenshot monitoring method described in the first aspect, which will not be repeated here.

[0099] The third aspect of the embodiment provides a computer device for executing the Web screenshot monitoring method described in the first aspect, comprising a memory, a processor and a transceiver which are sequentially communicatively connected, wherein the memory is used to store a computer program, the transceiver is used to receive and send messages, and the processor is used to read the computer program and execute the Web screenshot monitoring method described in the first aspect. Specifically, the memory can include but is not limited to random access memory (RAM), read-only memory (ROM), flash memory, first-in first-out memory (FIFO) and / or first-in last-out memory (FILO) and the like; the processor can use but is not limited to a microprocessor with model number STM32F105 series. In addition, the computer device can further include but is not limited to a power module, a display screen and other necessary components.

[0100] The working process, working details and technical effects of the computer device provided in the third aspect of the embodiment can be referred to the Web screenshot monitoring method in the first aspect, and will not be described here.

[0101] The fourth aspect of the embodiment provides a computer program product, including a computer program or instructions, which is used for realizing the Web screenshot monitoring method in the first aspect of the embodiment when being executed by a computer.

[0102] The working process, working details and technical effects of the computer program product provided in the embodiment can be referred to the Web screenshot monitoring method in the first aspect, and will not be described here.

[0103] Finally, it should be noted that: the above only describes the preferred embodiments of the present application, and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A web screenshot monitoring method, characterized by, The method comprises the following steps: determining whether an onblur event is triggered, if yes, obtaining the content of the clipboard, and determining whether there is an image in the content of the clipboard; if there is an image, determining whether there is a watermark in the image based on a watermark decoding algorithm, comprising: converting the image from an RGB color space to a YCbCr color space to obtain a converted image; extracting a channel from the converted image to obtain a color difference channel, the color difference channel comprising a Cb channel and a Cr channel; using a color difference amplification algorithm to amplify the color difference channel to obtain an amplified color difference image; using a watermark decoding algorithm to determine whether the amplified color difference image contains a watermark; if there is a watermark, using OCR to recognize the watermark to obtain recognition information; decrypting the recognition information to obtain decrypted information, and matching the decrypted information with original marking information, if the matching is successful, triggering a reporting mechanism, the original marking information being watermark marking information stored in an enterprise database.

2. The method of claim 1, wherein, using OCR to recognize the watermark to obtain recognition information, comprising: using an SSD algorithm to segment the watermark to obtain segmented character blocks; using a convolutional neural network to extract features from the segmented character blocks to obtain character features; using OCR to recognize the character features to obtain recognition information.

3. The method of claim 2, wherein, The convolutional neural network comprises a convolutional layer, an activation layer, a pooling layer and a fully connected layer, and the convolutional neural network is used to extract features from the segmented character blocks to obtain character features, comprising: the convolutional layer extracts local features from the segmented character blocks to obtain local character features; the activation layer uses an activation function to perform nonlinear transformation on the local character features to obtain transformed local character features; the pooling layer performs dimension reduction on the transformed local character features to obtain reduced local character features; the fully connected layer combines the reduced local character features to obtain character features.

4. The method of claim 1, wherein, Further comprising: obtaining a protection page, generating a watermark according to original marking information and a preset generation rule, encrypting the watermark using an encryption algorithm to obtain an encrypted watermark; embedding the encrypted watermark in the protection page, and dynamically adjusting the density of the encrypted watermark in the protection page using a watermark density adjustment algorithm based on user behavior.

5. The method of claim 4, wherein, Dynamically adjusting the density of the encrypted watermark in the protection page using a watermark density adjustment algorithm based on user behavior, comprising: extracting tags, elements and attributes of the protection page, and sorting the tags, elements and attributes of the protection page according to a preset importance rule to obtain an importance sequence; preliminarily adjusting the density of the encrypted watermark in the protection page according to the importance sequence; listening to page events of the user, the page events of the user including the time of the user's mouse staying, the frequency of the user's clicking and the moving track of the user's mouse; dynamically adjusting the density of the encrypted watermark in the protection page using a watermark density adjustment algorithm based on user behavior according to the page events of the user.

6. The method of claim 4, wherein, obtaining original marking information, generating a watermark and a timestamp corresponding to the watermark according to the original marking information and a preset generation rule; ​ A random number generator is used to generate a random private key; According to the timestamp corresponding to the watermark and the random private key, the watermark is encrypted using an encryption algorithm to obtain an encrypted watermark.

7. A web screenshot monitoring apparatus for implementing the web screenshot monitoring method according to any one of claims 1 to 6, characterized by Comprise: Picture judgment module, for judging whether the onBlur event is triggered, if yes, obtaining the content of the clipboard, judging whether there is a picture in the content of the clipboard; Watermark identification module, for if there is a picture, judging whether there is a watermark in the picture based on a watermark decoding algorithm, comprising: converting the picture from RGB color space to YCbCr color space to obtain a converted picture, extracting channels from the converted picture to obtain a color difference channel, the color difference channel comprising Cb channel and Cr channel, using a color difference amplification algorithm to amplify the color difference channel to obtain a picture after amplifying the color difference, using a watermark decoding algorithm to judge whether there is a watermark in the picture after amplifying the color difference, if there is a watermark, using OCR to identify the watermark to obtain identification information; Watermark matching module, for decrypting the identification information to obtain decrypted information, matching the decrypted information with original marking information, if the matching is successful, triggering a reporting mechanism, the original marking information being watermark marking information stored in an enterprise database.

8. A computer device, comprising: Comprise a memory, a processor and a transceiver connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to receive and send messages, and the processor is used to read the computer program and execute the Web screenshot monitoring method according to any one of claims 1-6.

9. A computer program product comprising computer programs or instructions, characterized in that, The computer program or the instruction realizes the Web screenshot monitoring method according to any one of claims 1-6 when executed by a computer.

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