Web screenshot monitoring method, device, equipment and product

Abstract

The invention discloses a Web screenshot monitoring method, device, equipment and product, and belongs to the technical field of data privacy, and the Web screenshot monitoring method comprises the following steps: judging whether an onBlur event is triggered or not, if so, obtaining the content of a clipboard, and judging whether a picture exists in the content of the clipboard or not; if the picture exists, judging whether a dark watermark exists in the picture based on a dark watermark decoding algorithm, and if the dark watermark exists, identifying the dark watermark by using OCR (Optical Character Recognition) to obtain identification information; and decrypting the identification information to obtain decrypted information, matching the decrypted information with original mark information, and if matching succeeds, triggering a reporting mechanism, the original mark information being dark watermark mark information stored in an enterprise database. According to the method, the dark watermark decoding algorithm and the OCR technology are used in cooperation, the identification information is matched with the original mark information, and therefore whether a user carries out screen capture operation or not is judged, the working efficiency is improved, the data safety is enhanced, and meanwhile effective tracing and management of important information are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of data privacy technology, and specifically relates to a Web screenshot monitoring method, device, equipment and product. Background Art

[0002] In recent years, with the development of Internet technology, screenshot sharing and copy-and-paste operations have become common, but at the same time, the problem of privacy leakage has also arisen. Malicious screenshots by users may cause intellectual property leakage, damage to brand image, leakage of trade secrets, and competitive disadvantages to the product. Among them, (1) Intellectual property leakage: If a company's products contain unique designs, undisclosed functions or sensitive information, if users share these contents through screenshots, the company's intellectual property may be leaked, affecting market competitiveness and may also cause legal disputes; (2) Brand image damage: If screenshots that are not authorized by the company are maliciously edited and disseminated, they may mislead the public and damage the company's brand image and reputation; (3) Trade secret leakage: For some products that provide customized services or process sensitive customer data, malicious screenshots by users may cause the leakage of trade secrets or customer privacy information, causing direct economic losses to the company and loss of customer trust; (4) Competitive disadvantage: The company's competitors can obtain information such as the product's interface design or functional details by collecting screenshots, and then imitate or improve it.

[0003] Existing methods for preventing users from maliciously taking screenshots primarily employ screen watermarks or screenshot blocking. However, these methods can lead to a poor user experience and reduced work efficiency. Therefore, providing an effective technical solution to address these issues has become a pressing challenge in the existing technology. Summary of the Invention

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

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for monitoring a web screen shot, comprising: Determine whether the onBlur event is triggered. If so, get the clipboard content and determine whether there is an image in the clipboard content; If there is a picture, determine whether there is a dark watermark in the picture based on the dark watermark decoding algorithm. If there is a dark watermark, use OCR to recognize the dark watermark and obtain identification information; The identification information is decrypted to obtain decrypted information, and the decrypted information is matched with the original marking information. If the match is successful, the reporting mechanism is triggered. The original marking information is the dark watermark marking information stored in the enterprise database.

[0006] In one possible design, based on a dark watermark decoding algorithm, determining whether a dark watermark exists in an image includes: Convert the image from RGB color space to YCbCr color space to obtain the converted image; Perform channel extraction on the converted image to obtain a color difference channel, wherein the color difference channel includes a Cb channel and a Cr channel; Use the color difference amplification algorithm to amplify the color difference channel to obtain the amplified color difference picture; The dark watermark decoding algorithm is used to determine whether the image after amplifying the color difference contains a dark watermark.

[0007] In a possible design, OCR is used to identify the dark watermark and obtain identification information, including: Use the SSD algorithm to segment the dark watermark and obtain the segmented character blocks; Use convolutional neural network to extract features from the segmented character blocks to obtain character features; Use OCR to recognize character features and obtain recognition information.

[0008] In one possible design, the convolutional neural network includes a convolution layer, an activation layer, a pooling layer, and a fully connected layer. The convolutional neural network is used to extract features from the segmented character blocks to obtain character features, including: The convolution layer extracts local features from the segmented character blocks to obtain local character features; The activation layer uses the activation function to perform nonlinear transformation on the local character features to obtain the transformed local character features; The pooling layer performs dimensionality reduction on the transformed local character features to obtain reduced-dimensional local character features; The fully connected layer combines the dimensionally reduced local character features to obtain character features.

[0009] In one possible design, it also includes: Obtain the protected page, generate a dark watermark according to the original marking information and the preset generation rule, and encrypt the dark watermark using an encryption algorithm to obtain an encrypted dark watermark; The encrypted dark watermark is embedded in the protection page, and the density of the encrypted dark watermark in the protection page is dynamically adjusted using a watermark density adjustment algorithm based on user behavior.

[0010] In a possible design, a watermark density adjustment algorithm based on user behavior is used to dynamically adjust the density of the encrypted dark watermark in the protected page, including: Extracting the tags, elements, and attributes of the protection page, and sorting the tags, elements, and attributes of the protection page by importance according to preset importance rules to obtain an importance sequence; Preliminarily adjust the density of the encrypted dark watermark in the protected page according to the importance sequence; Monitor user page events, including the time the user's mouse stays, the frequency of user clicks, and the movement trajectory of the user's mouse; According to the user's page events, the density of the encrypted dark watermark in the protected page is dynamically adjusted using a watermark density adjustment algorithm based on user behavior.

[0011] In a possible design, a dark watermark is generated according to the original marking information and a preset generation rule, and the dark watermark is encrypted using an encryption algorithm to obtain an encrypted dark watermark, including: Obtaining original marking information, and generating a dark watermark and a timestamp corresponding to the dark watermark according to the original marking information and a preset generation rule; Use a random number generator to generate a random private key; According to the timestamp and random private key corresponding to the dark watermark, the dark watermark is encrypted using an encryption algorithm to obtain an encrypted dark watermark.

[0012] In a possible design, before the dark watermark decoding algorithm is used to parse the image, denoising and image enhancement operations are also performed on the image.

[0013] In a second aspect, the present invention provides a Web screenshot monitoring device, which is used to implement the Web screenshot monitoring method described in the first aspect, comprising: The image judgment module is used to determine whether the onBlur event is triggered. If so, it obtains the content of the clipboard and determines whether there is an image in the content of the clipboard; The watermark recognition module is used to determine whether there is a dark watermark in the image based on the dark watermark decoding algorithm. If a dark watermark exists, the dark watermark is recognized using OCR to obtain identification information. The watermark matching module is used to decrypt the identification information, obtain the decrypted information, and match the decrypted information with the original marking information. If the match is successful, the reporting mechanism is triggered. The original marking information is the dark watermark marking information stored in the enterprise database.

[0014] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a transceiver that are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the Web screenshot monitoring method as described in any one of the above items.

[0015] In a fourth aspect, the present invention provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to execute any one of the Web screenshot monitoring methods described above.

[0016] The beneficial effects of the present invention are as follows: The present invention discloses a Web screenshot monitoring method, device, equipment, and product, including determining whether an onBlur event is triggered. If so, obtaining the content of a clipboard and determining whether there is an image in the content of the clipboard; if there is an image, determining whether there is a dark watermark in the image based on a dark watermark decoding algorithm; if there is a dark watermark, using optical character recognition (OCR) to identify the dark watermark, obtaining identification information, decrypting the identification information to obtain decrypted information, and matching the decrypted information with original marking information. If the match is successful, triggering a reporting mechanism, the original marking information is the dark watermark marking information stored in an enterprise database. The present invention determines whether there is a dark watermark in the screenshot using a dark watermark decoding algorithm, and uses OCR technology to identify it, matching the identified information with the original marking information, thereby determining whether the user has performed a screenshot operation, improving the user's page usage experience, while improving the enterprise's work efficiency, enhancing data security, and ensuring the effective traceability and management of important information. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a Web screenshot monitoring method provided in the first aspect of the embodiment; Figure 2 This is a module diagram of a Web screenshot monitoring device provided in the second aspect of the embodiment. DETAILED DESCRIPTION

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly introduced below in conjunction with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0019] It should be understood that the term "and / or" that may appear in this document is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B may indicate three situations: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" that may appear in this document describes another type of association object relationship, indicating that two relationships may exist. For example, A / and B may indicate two situations: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the previous and subsequent associated objects are in an "or" relationship.

[0020] Example: like Figure 1 As shown, the first aspect of this embodiment provides a method for monitoring a web (network) screenshot, including but not limited to the following steps: S1. Determine whether the onBlur event is triggered. If so, obtain the clipboard content and determine whether there is an image in the clipboard content; Specifically, the onBlur event occurs when an object loses focus, and is usually triggered during form validation, page optimization, or automatic saving.

[0021] S2. If the image exists, determine whether it contains a dark watermark based on the dark watermark decoding algorithm. If so, use Optical Character Recognition (OCR) to identify the dark watermark and obtain identification information. Specifically, in step S2, based on the dark watermark decoding algorithm, determining whether there is a dark watermark in the image includes: S21. Convert the image from the RGB color space to the YCbCr color space to obtain a converted image; Among them, the RGB color space is based on the three basic colors R (Red), G (Green) and B (Blue), and superimposes them to varying degrees to produce rich colors; in the YCbCr color space, Y is the brightness of the color, and Cb and Cr are the concentration offsets of blue and red.

[0022] S22. Perform channel extraction on the converted image to obtain a color difference channel, where the color difference channel includes a Cb channel and a Cr channel; S23. Amplify the color difference channel using a color difference amplification algorithm to obtain an amplified color difference image; In this embodiment, the color difference amplification algorithm is based on existing technologies and can employ linear stretching, histogram equalization, or adaptive histogram equalization. Linear stretching expands the image's color difference range through a linear transformation. Specifically, it maps each color value in the image to a new range, thereby increasing color contrast. Histogram equalization redistributes image pixel values ​​so that each color value appears with roughly the same frequency, thereby improving global image contrast. Adaptive histogram equalization performs equalization within a localized area of ​​the image, thereby improving contrast while avoiding excessive noise enhancement. These methods are not discussed in detail here.

[0023] S24. Use a dark watermark decoding algorithm to determine whether the image after amplifying the color difference has a dark watermark.

[0024] In this embodiment, the dark watermark decoding algorithm adopts a transform domain-based algorithm. Its principle is the embedding process: using Fourier transform or discrete cosine transform to convert the image from the spatial domain to the frequency domain, and embedding the watermark information in the high-frequency or medium-frequency sparse; the extraction process: performing an inverse transform on the watermarked image and extracting the watermark information from the frequency domain coefficients.

[0025] Specifically, in step S2, the second dark watermark is identified using OCR to obtain identification information, including: S25. Use the SSD algorithm (Single Shot MultiBox Detector, target detection algorithm) to segment the second dark watermark to obtain segmented character blocks; S26. Using a convolutional neural network to extract features from the segmented character blocks to obtain character features; 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: S261. The convolution layer extracts local features from the segmented character blocks to obtain local character features; S262. The activation layer performs a nonlinear transformation on the local character features using an activation function to obtain the transformed local character features; S263. The pooling layer performs a dimensionality reduction operation on the transformed local character features to obtain reduced-dimensionality local character features; S264. The fully connected layer combines the dimensionally reduced local character features to obtain character features.

[0026] Furthermore, before the dark watermark decoding algorithm is used to parse the image, denoising and image enhancement operations are also performed on the image.

[0027] In this embodiment, denoising and image enhancement operations are performed to reduce the impact of noise and enhance image features to facilitate subsequent recognition and matching of dark watermarks on the image.

[0028] S27. Use OCR to identify character features and obtain recognition information.

[0029] S3. Decrypt the identification information to obtain decrypted information, and match the decrypted information with the original tag information. If the match is successful, the reporting mechanism is triggered.

[0030] In one possible design, the reporting mechanism includes reporting suspected screenshot behavior and recording it in a log so that further measures can be taken. After a successful match, it also includes using an early warning system to issue an early warning of suspected screenshot behavior, reminding the company of the risk of data leakage.

[0031] In a possible design, the method further includes obtaining a protected page, generating a dark watermark according to original marking information and a preset generation rule, and encrypting the dark watermark using an encryption algorithm to obtain an encrypted dark watermark; The encrypted dark watermark is embedded in the protection page, and the density of the encrypted dark watermark in the protection page is dynamically adjusted using a watermark density adjustment algorithm based on user behavior.

[0032] Among them, the original marking information is the dark watermark marking information stored in the enterprise database, and the dark watermark marking information includes but is not limited to the enterprise name and product information. The preset generation rules are the dark watermark rules set by the user, which can be exemplified by text format and / or watermark form, etc.

[0033] Specifically, a watermark density adjustment algorithm based on user behavior is used to dynamically adjust the density of the encrypted dark watermark in the protected page, including: Extracting the tags, elements, and attributes of the protection page, and sorting the tags, elements, and attributes of the protection page by importance according to preset importance rules to obtain an importance sequence; Preliminarily adjust the density of the encrypted dark watermark in the protected page according to the importance sequence; Monitor user page events, including the time the user's mouse stays, the frequency of user clicks, and the movement trajectory of the user's mouse; According to the user's page events, the density of the encrypted dark watermark in the protected page is dynamically adjusted using a watermark density adjustment algorithm based on user behavior.

[0034] The preset importance rules are rules for the importance of tags, elements and attributes, and their arrangement order set by users.

[0035] The aforementioned disclosure employs a user-behavior-based watermark density adjustment algorithm to adjust the density of the encrypted dark watermark on a page. This algorithm determines the importance of a region based on user behavior. If the region is highly important, the density of the encrypted dark watermark is increased; if the region is less important, the density of the encrypted dark watermark is decreased. Adjusting the density distribution of the dark watermark facilitates its recognition by the dark watermark decoding algorithm and optical character recognition (OCR).

[0036] Furthermore, a dark watermark is generated according to the original marking information and a preset generation rule, and the dark watermark is encrypted using an encryption algorithm to obtain an encrypted dark watermark, including: Obtaining original marking information, and generating a dark watermark and a timestamp corresponding to the dark watermark according to the original marking information and a preset generation rule; Use a random number generator to generate a random private key; According to the timestamp and random private key corresponding to the dark watermark, the dark watermark is encrypted using an encryption algorithm to obtain an encrypted dark watermark.

[0037] This embodiment provides a Web screenshot monitoring method, which has the following beneficial effects: (1) Enhance data security: Using technical means to indirectly restrict screenshots can reduce the risk of information leakage and provide users with a safer service environment; (2) Data-driven decision-making: By monitoring and analyzing user behavior, companies can gain important insights into how to optimize their products and services; (3) Preventing potential threats: Early warning systems can help companies quickly respond to any suspicious activities and reduce the possibility of risks; (4) Improve work efficiency: Use within the enterprise can also help manage employee behavior, especially in the context of increasing popularity of remote work to ensure efficient execution of work processes.

[0038] like Figure 2 As shown, the second aspect of this embodiment provides a Web screenshot monitoring device, including: The image judgment module is used to determine whether the onBlur event is triggered. If so, it obtains the content of the clipboard and determines whether there is an image in the content of the clipboard; The watermark recognition module is used to determine whether there is a dark watermark in the image based on the dark watermark decoding algorithm. If a dark watermark exists, the dark watermark is recognized using OCR to obtain identification information. The watermark matching module is used to decrypt the identification information, obtain the decrypted information, and match the decrypted information with the original marking information. If the match is successful, the reporting mechanism is triggered. The original marking information is the dark watermark marking information stored in the enterprise database.

[0039] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be found in the Web screenshot monitoring method described in the first aspect, and will not be described in detail here.

[0040] A third aspect of this 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 communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the web screenshot monitoring method described in the first aspect. Specifically, the memory may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-input first-output (FIFO), and / or first-input last-output (FILO) memory, etc.; the processor may include, but is not limited to, a microprocessor from the STM32F105 series. Furthermore, the computer device may include, but is not limited to, a power module, a display screen, and other necessary components.

[0041] The working process, working details and technical effects of the aforementioned computer device provided in the third aspect of this embodiment can be found in the Web screenshot monitoring method described in the first aspect, and will not be described in detail here.

[0042] A fourth aspect of this embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, are used to implement the Web screenshot monitoring method as described in the first aspect of the embodiment.

[0043] The working process, working details and technical effects of the aforementioned computer program product provided in this embodiment can be found in the Web screenshot monitoring method described in the first aspect, and will not be described in detail here.

[0044] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A Web screenshot monitoring method, characterized in that: include: Determine whether the onBlur event is triggered. If so, get the clipboard content and determine whether there is an image in the clipboard content; If there is a picture, based on the dark watermark decoding algorithm, determine whether there is a dark watermark in the picture. If there is a dark watermark, use OCR to recognize the dark watermark and obtain identification information; The identification information is decrypted to obtain decrypted information, and the decrypted information is matched with the original marking information. If the match is successful, the reporting mechanism is triggered. The original marking information is the dark watermark marking information stored in the enterprise database.

2. A Web screen capture monitoring method according to claim 1, characterized in that: Based on the dark watermark decoding algorithm, determine whether there is a dark watermark in the image, including: Convert the image from RGB color space to YCbCr color space to obtain the converted image; Perform channel extraction on the converted image to obtain a color difference channel, wherein the color difference channel includes a Cb channel and a Cr channel; Use the color difference amplification algorithm to amplify the color difference channel to obtain the amplified color difference picture; The dark watermark decoding algorithm is used to determine whether the image after amplifying the color difference contains a dark watermark.

3. A Web screen capture monitoring method according to claim 1, characterized in that: Use OCR to identify dark watermarks and obtain identification information, including: Use the SSD algorithm to segment the dark watermark and obtain the segmented character blocks; Use convolutional neural network to extract features from the segmented character blocks to obtain character features; Use OCR to recognize character features and obtain recognition information.

4. A Web screen capture monitoring method according to claim 3, characterized in that: The convolutional neural network includes a convolution layer, an activation layer, a pooling layer, and a fully connected layer. The convolutional neural network is used to extract features from the segmented character blocks to obtain character features, including: The convolution layer extracts local features from the segmented character blocks to obtain local character features; The activation layer uses the activation function to perform nonlinear transformation on the local character features to obtain the transformed local character features; The pooling layer performs dimensionality reduction on the transformed local character features to obtain reduced-dimensional local character features; The fully connected layer combines the dimensionally reduced local character features to obtain character features.

5. A Web screen capture monitoring method according to claim 1, characterized in that: Also includes: Obtain the protected page, generate a dark watermark according to the original marking information and the preset generation rule, and encrypt the dark watermark using an encryption algorithm to obtain an encrypted dark watermark; The encrypted dark watermark is embedded in the protection page, and the density of the encrypted dark watermark in the protection page is dynamically adjusted using a watermark density adjustment algorithm based on user behavior.

6. A Web screen capture monitoring method according to claim 5, characterized in that: The density of the encrypted dark watermark in the protected page is dynamically adjusted using a watermark density adjustment algorithm based on user behavior, including: Extracting the tags, elements, and attributes of the protection page, and sorting the tags, elements, and attributes of the protection page by importance according to preset importance rules to obtain an importance sequence; Preliminarily adjust the density of the encrypted dark watermark in the protected page according to the importance sequence; Monitor user page events, including the time the user's mouse stays, the frequency of user clicks, and the movement trajectory of the user's mouse; According to the user's page events, the density of the encrypted dark watermark in the protected page is dynamically adjusted using a watermark density adjustment algorithm based on user behavior.

7. A Web screen capture monitoring method according to claim 5, characterized in that: A dark watermark is generated according to the original marking information and the preset generation rules, and the dark watermark is encrypted using an encryption algorithm to obtain an encrypted dark watermark, including: Obtaining original marking information, and generating a dark watermark and a timestamp corresponding to the dark watermark according to the original marking information and a preset generation rule; Use a random number generator to generate a random private key; According to the timestamp and random private key corresponding to the dark watermark, the dark watermark is encrypted using an encryption algorithm to obtain an encrypted dark watermark.

8. A Web screenshot monitoring device, used to implement the Web screenshot monitoring method according to any one of claims 1 to 7, characterized in that: include: The image judgment module is used to determine whether the onBlur event is triggered. If so, it obtains the content of the clipboard and determines whether there is an image in the content of the clipboard; The watermark recognition module is used to determine whether there is a dark watermark in the image based on the dark watermark decoding algorithm. If a dark watermark exists, the dark watermark is recognized using OCR to obtain identification information. The watermark matching module is used to decrypt the identification information, obtain the decrypted information, and match the decrypted information with the original marking information. If the match is successful, the reporting mechanism is triggered. The original marking information is the dark watermark marking information stored in the enterprise database.

9. A computer device, characterized in that: The method comprises a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive 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 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or the instruction is executed by a computer, the Web screenshot monitoring method according to any one of claims 1 to 7 is implemented.

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