A double-verification driven book page number watermark anti-theft method

Abstract

The application discloses a double-verification driven book page number watermark anti-theft method, and relates to the technical field of digital watermark anti-fake, which comprises watermark information generation, watermark embedding, double-verification and watermark life cycle management steps: based on the hash value of the page number text content and the encrypted purchaser identifier, the unique dynamic watermark information of each page of each book is generated; the same is embedded in the corresponding area of the page number in the form of a semi-transparent visible watermark and an implicit watermark, the implicit watermark adopts a lightweight embedding algorithm optimized for binary text images and is processed by error correction coding; through the cooperative work of human eye rapid verification and scanning equipment accurate verification, the book authenticity judgment, page content integrity check and pirate tracing are realized; the central verification platform maintains the identity binding relationship, supports the watermark remote invalidation and the identity rebinding after the book transfer, the application takes into account the reading experience and the privacy protection, is suitable for various application scenes, and has reliable anti-fake effect.

Description

Technical Field

[0001] This invention relates to the field of digital watermarking anti-counterfeiting technology, specifically a dual-verification driven book page number watermarking anti-theft method. Background Technology

[0002] With the accelerated digitization of printed books, the illegal copying, scanning, and alteration of page numbers have become increasingly rampant, seriously infringing upon the rights of copyright holders and legitimate purchasers. While existing anti-counterfeiting technologies based on digital watermarking are widely used in electronic document and image protection, their application on the specific medium of printed book page numbers still suffers from insufficient robustness, limited verification methods, and inability to link to specific user identities.

[0003] Among existing patent technologies, CN102024167A, a dual-verification anti-counterfeiting method based on pattern hiding, proposes to achieve primary anti-counterfeiting through diffraction grating encryption and dual-card overlap verification, and to use laser irradiation for secondary verification. While this method possesses a dual-verification approach, it relies on specific physical optical components and dedicated laser equipment, resulting in a complex verification process, high costs, and difficulty in dynamically binding it to the page structure and user identity information of printed books. Another patent, CN118799158A, describes a method, device, equipment, and medium for embedding anti-counterfeiting watermark information. It embeds watermarks by constructing a three-dimensional mesh model and extracting a feature matrix, focusing on improving the concealment of digital image watermarks. However, it does not address the problem of watermark extraction failure due to pixel distortion and geometric deformation during the printing-scanning process of printed page numbers, and lacks support for content integrity verification and piracy tracing. Furthermore, existing technologies such as anti-printing and scanning watermarking algorithms based on spread spectrum coding, while able to resist some geometric distortion, have limited watermark capacity and are insufficient to accommodate dynamically changing user identity data on each page.

[0004] The technical problem with the existing technologies is how to create a unique identity fingerprint for each page of every printed book. This fingerprint needs to support both rapid initial screening on-site and precise traceability in the background, and can verify the integrity of the page content. Existing methods cannot achieve rapid initial verification without dedicated equipment, nor can they dynamically bind page content hashes with user identifiers to form a dual-verification mechanism, resulting in weak piracy traceability and insufficient anti-tampering performance.

[0005] Therefore, there is an urgent need for a watermarking anti-theft method that targets the characteristics of page numbers in paper books, and that can balance convenience and security through a dual verification mechanism while maintaining low cost. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a dual-verification driven method for preventing book page number watermarking. By generating a unique dynamic watermark that integrates the page number hash value and the encrypted buyer's identity, embedding both visible and implicit dual watermarks, and combining rapid human verification with precise device verification, along with watermark lifecycle management on a central platform, the robustness of paper book page number watermarks can be improved, enabling rapid initial screening of authenticity, verification of content integrity, and accurate piracy tracing, adapting to multiple purchasing scenarios.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a dual-verification driven book page number watermarking anti-theft method, comprising the following steps:

[0008] Step 1: Watermark Information Generation: Calculate the hash value based on the page number text content and combine it with the buyer's identity identifier to generate dynamic watermark information, wherein the dynamic watermark information is unique for each page and each book;

[0009] Step 2, Watermark Embedding: The dynamic watermark information is embedded into the book page number, including generating a visible watermark and an implicit watermark. The visible watermark contains part of the plaintext of the user's identity identifier or a specific code and is placed in a semi-transparent manner in the header, footer, or sidebar area of ​​the page number. The implicit watermark contains complete dynamic watermark information and is encoded into the character edges, stroke connections, or microstructure of the background texture of the page number through a lightweight embedding algorithm.

[0010] Step 3, Dual Verification: This includes rapid verification and precise verification. Rapid verification makes a preliminary judgment on authenticity by directly observing the presence and content of the visible watermark. Precise verification obtains page number images through a scanning device and extracts the implicit watermark to perform content integrity verification and piracy tracing. Content integrity verification is achieved by comparing the extracted hash value with the real-time calculated hash value of the page number text content. Piracy tracing is achieved by querying the user identity identifier and record information extracted from the central verification platform.

[0011] Furthermore, in step one, the dynamic watermark information is composed of the hash value of the page number text content and the encrypted purchaser's identity identifier. The hash value is calculated using the secure hash algorithm SHA-256, and its mathematical expression is:

[0012] ;

[0013] in, This represents the output 256-bit hash value. The input message represents the page number text content. The buyer's identity identifier is an encrypted digest generated based on the user's order number or account. The encryption is completed in a central database using the AES encryption algorithm.

[0014] Furthermore, the visual watermark generation includes combining partial information of the user's identity identifier with copyright symbols and book unique identifiers to form visual text or graphics. The visual text or graphics undergo semi-transparency processing and aesthetic design to adapt to the page number layout in a way that does not affect normal reading. Moreover, the visual watermark is directly superimposed on the page number layout file during the printing process to form a warning message that can be recognized by the human eye.

[0015] Furthermore, the implicit watermark embedding uses a lightweight embedding algorithm optimized for binary text images. This algorithm analyzes the binary image of the page number, selects character outline edges, stroke gaps, or background dot density as embedding regions, and encodes watermark information by fine-tuning pixel values ​​or texture changes. The embedding process includes error-correcting encoding of the watermark data to improve robustness. This error-correcting encoding uses Reed-Solomon codes, and its encoding process is defined by the following mathematical expression:

[0016] ;

[0017] in, This represents the codeword polynomial generated after encoding. This represents a data polynomial composed of the original watermark data. This represents the total number of symbols in a Reed-Solomon codeword. The number of signs representing the data portion. Represents the generator polynomial of the Reed-Solomon code. The shift operator represents the power operation of a data polynomial, with the symbol... The symbol represents polynomial multiplication, and the symbol represents modular multiplication.

[0018] Furthermore, the content integrity verification is performed in the precise verification step, which includes obtaining the hash value of the page number text content from the extracted implicit watermark, and simultaneously calculating the hash value of the current page number text content in real time by scanning the image. If the two hash values ​​are inconsistent, it is determined that the page number content has been tampered with. The real-time calculation uses the same hash algorithm as the watermark information generation step to ensure consistency in comparison.

[0019] Furthermore, the piracy tracing is performed in the precise verification step, which includes decrypting the user identity identifier from the extracted implicit watermark and sending the identifier to the central verification platform for querying. The central verification platform returns the purchaser information corresponding to the identifier, thereby locating the initial source of the pirated books. The query process is completed under the premise of privacy authorization, and the central verification platform records the binding status of the book's unique identifier and the user identity identifier.

[0020] Furthermore, in step three, rapid verification and precise verification work together. Rapid verification is a device-free operation, completed by human eyes checking the visible watermark in a well-lit environment. Precise verification requires the use of a scanning device or mobile application to obtain the page number image, and extracts and analyzes the implicit watermark using a dedicated algorithm. The verification results of the two are cross-checked. If the visible watermark and the implicit watermark information do not match, it is determined to be a counterfeit.

[0021] Furthermore, in step two, the embedding position of the implicit watermark is adaptively selected based on local page number features, including analyzing the text layout and character structure, and prioritizing micro-adjustments at character edges or stroke connections. These adjustments are achieved by modifying pixels or introducing texture changes, and the adjustment range is controlled within the range imperceptible to the human eye to ensure the watermark's concealment and readability.

[0022] Furthermore, the method also includes step four, watermark lifecycle management, in which the central verification platform maintains the binding relationship between the unique identifier of the book and the user's identity identifier, and activates the status after the book is sold, supports a remote invalidation mechanism, and when a book is reported lost, the platform adds the corresponding identifier to the blacklist and prompts an abnormal status during the verification process.

[0023] Furthermore, the scanning device used in step three includes a regular scanner or a mobile phone with a macro lens. The mobile application integrates image processing and decoding functions to automatically extract implicit watermarks and execute verification logic. The central verification platform is a cloud database system that provides application programming interfaces for verification queries and supports crowdsourced verification nodes to upload anonymous verification data to monitor the circulation path of pirated goods.

[0024] Compared with existing technologies, this dual-verification driven book page number watermarking anti-theft method has the following advantages:

[0025] I. This invention generates a unique dynamic watermark for each page of each book by calculating a hash value based on the page number text content and combining it with an encrypted purchaser's identity identifier. This information is then embedded into the corresponding area of ​​the page number in the form of both a visible and an implicit watermark. Combined with a collaborative mechanism of rapid and precise verification, this invention successfully creates a unique identity fingerprint for each page of each book. This supports both rapid on-site screening without the need for specialized equipment and page number content integrity verification and accurate piracy tracing through scanning devices, providing reliable anti-counterfeiting protection for copyright holders and legitimate purchasers.

[0026] Second, this invention optimizes the adaptive embedding strategy of implicit watermarks by adopting mature encryption algorithms and error correction coding technology. While ensuring the concealment of the watermark, it enhances its ability to resist printing and scanning distortion without affecting the normal reading experience of the book. Furthermore, by relying on a central verification platform, it realizes full life-cycle management of the watermark, supports remote invalidation after the book is reported lost and identity rebinding during legal transfer, and is suitable for various online and offline purchasing scenarios.

[0027] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0029] Figure 1 This is a flowchart of the steps of the present invention;

[0030] Figure 2 This is a diagram of the dual-verification watermarking system architecture of the present invention. Detailed Implementation

[0031] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0032] Example 1

[0033] like Figure 1 and Figure 2 As shown, this embodiment, as a basic embodiment, corresponds to the technical solution defined in the independent claims of this invention, and elaborates in detail the complete implementation process of a dual-verification driven book page number watermarking anti-theft method. This embodiment clarifies the specific execution method of each step, the implementation logic of the algorithm used, and the parameter definitions. The specific implementation content is as follows.

[0034] Watermark Information Generation: The core purpose of watermark information generation is to generate a unique dynamic watermark for each page of each book. This dynamic watermark information needs to be associated with the page number text content and the buyer's identity to provide basic data support for subsequent verification and traceability.

[0035] Specifically, page number text content refers to the text information directly related to the page number on the corresponding page of the book, including the page number itself and the page-specific auxiliary identification text. This information constitutes the input message for calculating the hash value. When generating the hash value, the secure hash algorithm SHA256 is used to calculate the page number text content. Its mathematical expression is:

[0036] ;

[0037] in, The output hash value has a fixed length of 256 bits. This length of hash value has sufficient uniqueness and collision resistance, and can effectively distinguish the text content of different page numbers. The input message is the page number text content, namely the aforementioned page number and page-specific auxiliary identification text. The SHA256 algorithm is a mature existing hash algorithm. This embodiment directly adopts the standard implementation logic of this algorithm without any additional modifications. Simply input the page number text content into the algorithm according to the standard input format to obtain the corresponding hash value. This process ensures the consistency and reliability of hash value calculation.

[0038] The buyer's identity identifier is generated based on the user's order number or account. It is encrypted in a central database using the AES encryption algorithm, ultimately forming a cryptographic digest that serves as the buyer's identity identifier. The AES encryption algorithm employs a standard encryption process, using the encryption key stored in the central database to encrypt the order number or account. The encryption process conforms to industry standards for the AES algorithm, ensuring the security of the buyer's identity information and preventing its leakage.

[0039] The dynamic watermark information is formed by concatenating the hash value calculated above and the encrypted buyer's identifier in a preset order. The concatenation order can be preset in the system; for example, the hash value can be concatenated first, followed by the buyer's identifier. No additional separators are needed during the concatenation process; the fixed length of both elements allows for accurate splitting during subsequent extraction. Since the hash value is generated based on the page number text content of each page, and the buyer's identifier is generated based on the order number or account of different buyers, the concatenated dynamic watermark information is unique for each page and each book, providing a unique identification basis for subsequent anti-counterfeiting verification and piracy tracing.

[0040] Watermark embedding: The core of the watermark embedding process is to embed dynamic watermark information into the book page numbers in two forms: visible watermark and implicit watermark. This ensures both the convenience of quick verification and the security and integrity of accurate verification, while not affecting the normal reading of the book.

[0041] Visual watermark generation and embedding: The process of generating a visual watermark involves combining partial information from the buyer's identity identifier with a copyright symbol and a unique book identifier to form visual text or graphics. The partial information from the buyer's identity identifier refers to certain characteristic information extracted from the encrypted buyer's identity identifier. This information does not involve the buyer's private data but serves as a preliminary verification identifier. The unique book identifier is a unique identifier assigned to each book at the time of manufacture to distinguish different books. The copyright symbol uses an industry-standard copyright mark to clearly indicate the copyright ownership of the book.

[0042] The visual text or graphics formed by combining the above three elements need to undergo semi-transparency processing and aesthetic design. Semi-transparency processing is achieved by adjusting the transparency of the visual text or graphics. The adjustment range is such that the human eye can clearly see the text or graphics without obscuring the page numbers or other content on the page, ensuring that normal reading is not affected. Aesthetic design involves adjusting the font, size, and style of the visual text or graphics according to the layout style of the book's page numbers, so that they blend into the overall visual effect of the page number area.

[0043] The embedding of visual watermarks is completed during the book printing process. Specifically, processed visual text or graphics are directly overlaid onto the page layout file. The overlay position is chosen to be in the header, footer, or sidebar area of ​​the page number. This area does not affect the reading of the main content of the page, yet it is easy for users to observe during quick verification. The overlaid visual watermark forms a warning message that can be recognized by the human eye, which can not only deter illegal copyists but also provide an intuitive basis for quick verification.

[0044] Implicit watermark generation and embedding: Implicit watermarks contain complete dynamic watermark information. The embedding process uses a lightweight embedding algorithm optimized for binary text images. This algorithm can improve the robustness of the watermark in the printing and scanning process while ensuring the watermark's concealment.

[0045] First, the binary image of the page number is analyzed. A binary image refers to an image where the page number area contains only black and white pixels. Image analysis techniques are used to obtain information about the text layout and character structure of the page number, including the position and distribution of characters, the shape of character outlines, the size of stroke gaps, and the density of background dots. Based on these analysis results, an embedding region is adaptively selected, prioritizing the edges of character outlines or the connections between strokes. Pixel changes in these areas are less noticeable to the human eye and can better resist geometric and pixel distortions during printing and scanning.

[0046] Before embedding watermark information, the dynamic watermark data needs to be error-corrected and encoded. Reed-Solomon coding is used as the error-correction coding method, and its mathematical expression is as follows:

[0047] ;

[0048] in, The codeword polynomial generated after encoding; The original dynamic watermark data is converted into polynomial coefficients according to a preset encoding rule to form a data polynomial. The total number of symbols in a Reed-Solomon codeword; The number of signs in the data portion. and The value is determined based on the length of the dynamic watermark data and the error correction requirements, ensuring that the encoded codewords can meet the capacity requirements of watermark embedding, while also having sufficient error correction capabilities. The generator polynomial for Reed-Solomon codes is based on an industry-standard generator polynomial to ensure the standardization and compatibility of the encoding process. This is a shift operator for exponentiation of a data polynomial. Its function is to increase the degree of the data polynomial, reserving space for the embedding of error-correcting codes; (symbol) Represents polynomial multiplication, symbol This represents a modulo operation. This error-correction coding process effectively compensates for potential watermark data loss or errors during printing and scanning, improving the robustness of implicit watermarks.

[0049] After error correction encoding, watermark information is encoded by fine-tuning the pixel values ​​of the embedded region or introducing texture variations. Fine-tuning pixel values ​​refers to making tiny adjustments to the grayscale values ​​of some pixels in the selected embedded region, with the adjustment range controlled within a range imperceptible to the human eye. Introducing texture variations refers to adding subtle texture features to the microstructure of the background texture. These texture features carry the watermark information and do not affect the normal display of page numbers. The entire embedding process ensures the concealment of the implicit watermark while guaranteeing that the watermark information can be accurately identified in the subsequent extraction process.

[0050] Dual verification: Dual verification includes rapid verification and precise verification. The two work together to cross-check each other and ensure the accuracy of the verification results. This enables a preliminary judgment and precise verification of the authenticity of the book, while also completing the verification of content integrity and tracing the source of piracy.

[0051] Rapid Verification: Rapid verification is a device-free operation, requiring no specialized equipment. Users or verification personnel can directly observe the visible watermark in the page number area in a well-lit environment. The observation includes the presence or absence of the visible watermark and its content characteristics, such as the combination of the copyright symbol and the book's unique identifier, as well as features of the buyer's identification information.

[0052] If a visible watermark matching the preset characteristics can be clearly observed, the book can be preliminarily determined to be genuine; if no visible watermark is observed or the observed watermark does not match the preset characteristics, the book can be preliminarily determined to be suspected of being pirated. The advantage of rapid verification is that it is convenient to operate and does not take much time, enabling rapid initial screening of the authenticity of books. It is suitable for daily inspections in book sales channels and preliminary identification by consumers.

[0053] Precise verification: Precise verification requires the use of scanning devices or mobile applications. Scanning devices include ordinary scanners or mobile phones with macro lenses. Mobile applications integrate image processing and decoding functions, which can automatically extract implicit watermarks and execute verification logic.

[0054] First, acquire page number images using a scanning device or mobile application. Ensure the image is clear during acquisition to avoid watermark extraction failure due to blurriness. Then, preprocess the acquired page number images using the scanning device or mobile application, including image enhancement, noise reduction, and binarization. Preprocessed images are easier to extract from implicit watermarks.

[0055] Implicit watermarks are extracted using a specialized algorithm corresponding to the implicit watermark embedding algorithm. This algorithm accurately identifies pixel value changes or texture changes in the embedding area, thereby extracting the complete encoded dynamic watermark information. After extraction, the dynamic watermark information is decoded, including splitting the hash value and the encrypted buyer identification identifier. The buyer identification identifier is then decrypted using the AES decryption algorithm corresponding to the encryption process, supported by a central verification platform to ensure the security and accuracy of the decryption.

[0056] Content integrity verification is achieved by comparing the extracted hash value with the real-time calculated hash value of the page number text content. During real-time calculation, the same SHA256 algorithm as used in the watermark information generation process is employed to calculate the hash value of the page number text content in the scanned image, yielding a real-time hash value. The extracted hash value is then compared with the real-time hash value. If they match, the page number content has not been tampered with; if they do not match, the page number content has been tampered with, and the book is suspected of being pirated or altered.

[0057] Piracy tracing is achieved by querying user identification identifiers and records extracted from a central verification platform. This central verification platform is a cloud database system that stores the binding relationship between unique book identifiers and user identification identifiers, as well as relevant purchase records. With privacy authorization, the decrypted user identification identifier is sent to the central verification platform. The platform then uses this identifier to query the corresponding purchaser information, including purchase time and channel, thereby pinpointing the initial source of the pirated books. Simultaneously, the central verification platform records the query process and verification results, and supports crowdsourced verification nodes uploading anonymous verification data to monitor the circulation path of pirated books.

[0058] The dual verification works collaboratively: the results of rapid verification and precise verification cross-check each other. If the visual watermark observed in rapid verification matches the relevant information contained in the implicit watermark extracted in precise verification (e.g., the unique identifier of the book is consistent), the book is further confirmed to be genuine. If the visual watermark and implicit watermark information do not match (e.g., the unique identifier of the book in the visual watermark does not match the unique identifier of the book extracted from the implicit watermark), it is determined to be counterfeit. Through the collaborative work of the two, both the convenience and accuracy of verification are ensured, effectively improving the reliability of anti-piracy verification.

[0059] Watermark lifecycle management: The central verification platform maintains the binding relationship between the unique identifier of the book and the user's identity identifier. After the book is sold, the central verification platform activates the corresponding binding relationship. At this time, the watermark information is in a valid state and can be verified normally.

[0060] The system supports a remote verification mechanism. When a book is reported lost, the purchaser can submit a loss report to the central verification platform through designated channels. After verifying the relevant information, the central verification platform will blacklist the identifier corresponding to the book. During subsequent verification, if the page watermark information of the book is scanned, the central verification platform will indicate an abnormal status, informing the verifier that the book is a reported lost book and may have been stolen or illegally copied. This further improves the anti-piracy system and reduces the loss of legitimate rights and interests.

[0061] This embodiment, through the above technical solution, generates a unique dynamic watermark information for each page of each paper book. Through the dual embedding of visible and implicit watermarks, as well as the collaborative work of rapid verification and accurate verification, it can achieve both rapid initial screening of book authenticity and completion of content integrity verification and accurate piracy tracing.

[0062] The use of mature hash encryption and error correction coding algorithms ensures the feasibility and reliability of the technical solution, and those skilled in the art can reproduce the entire technical process based on the description in this embodiment. Furthermore, the embedding position and method of the watermark fully consider the reading experience of printed books, without affecting normal reading. The verification process does not require specialized and complex equipment, reducing verification costs.

[0063] Compared with existing technologies, the technical solution of this embodiment improves the robustness of page number watermarks in printed books, enhances the convenience and accuracy of anti-piracy verification, and can, to a certain extent, curb the illegal copying and tampering of page number content, protecting the rights and interests of copyright holders and legitimate purchasers. The technical solution has a reasonable structure, smooth transitions between steps, and possesses the conditions and value for practical application.

[0064] Example 2

[0065] like Figure 1 and Figure 2 As shown, this embodiment is an optimized embodiment. Based on the basic technical solution of Embodiment 1, it supplements the implementation of the identity identification flexibility, implicit watermark embedding adaptability, crowdsourcing verification mechanism and watermark lifecycle extension scenarios mentioned in the claims but not elaborated in Embodiment 1, so as to further improve the practicality and adaptability of the technical solution. The specific implementation content is as follows.

[0066] Optimized implementation of watermark information generation: Based on the first embodiment, this embodiment optimizes the generation method of the buyer's identity identifier and increases the adaptation scenarios of the identity identifier to meet the identity binding requirements of different purchase modes.

[0067] Specifically, in Embodiment 1, the buyer's identity identifier is generated based on the order number or account. This embodiment supplements this with an "authorized buyer device identifier" as an alternative generation source. In some optional implementations, when a buyer purchases books through offline channels without registering an account, an authorized device identifier can be provided via the buyer's mobile device. After receiving the authorized device identifier, the central database encrypts it using the same AES encryption algorithm as in Embodiment 1 to generate a buyer's identity identifier with the same encrypted format as the order number / account.

[0068] Understandably, the core of this optimization is to expand the sources of identity identifiers without changing the encryption algorithm and the final format of the identifier. Its implementation requires meeting two key conditions: first, the acquisition of the device identifier must be explicitly authorized by the purchaser to avoid privacy violations; second, the encrypted device identifier must maintain the same length and format as the encrypted order number / account to ensure compatibility with subsequent watermark splicing and extraction. This optimization aims to adapt to scenarios such as offline purchases without an account or temporary purchases, improving the technical solution's adaptability to different sales channels and avoiding the problem of identity identifiers failing to be generated due to purchase scenario limitations.

[0069] The other steps for generating watermark information are completely consistent with those in Example 1, ensuring the consistency and compatibility of the technical solution.

[0070] Optimized implementation of watermark embedding: This embodiment focuses on optimizing the adaptive embedding process of implicit watermarks, further improving the robustness of implicit watermarks under different printing processes and different font page numbers. This optimization corresponds to the technical feature in the claim that "the embedding position of the implicit watermark is adaptively selected based on the local features of the page number".

[0071] Specifically, a "page number text feature analysis step" is added before implicit watermark embedding. First, the image processing module extracts features from the binary image of the page numbers, including the font type, stroke thickness, and character spacing of the page number characters. Based on these features, the image processing module further determines the "pixel redundancy" of different regions—that is, the maximum range in which pixel adjustments in a region will not affect human visual recognition. For example, for characters with higher stroke thickness, there is more adjustable pixel space at the stroke edges, resulting in higher pixel redundancy; for characters with thinner strokes, the gaps between characters are prioritized, as pixel adjustments in these areas are less likely to disrupt character integrity.

[0072] After determining the embedding area, this embodiment uses "grayscale gradient fine-tuning" instead of "pixel value modification" in Embodiment 1. Specifically, based on the encoding requirements of the watermark data, the pixel grayscale values ​​of the embedding area are adjusted in a gradient manner, with the adjustment range controlled within 10-15 grayscale levels. For example, in the character edge area, the pixel grayscale value is gradient-transitioned from 255 to 240 along the stroke direction, with each gradient corresponding to 1 bit of watermark data. This method is used to encode the watermark information.

[0073] The optimization achieves the following: Firstly, by analyzing font and stroke features, it ensures that the selection of the embedding area is more in line with the actual printing characteristics of the page number, avoiding watermark loss or character distortion caused by improper embedding area; secondly, compared with direct pixel modification, grayscale gradient fine-tuning is more resistant to color distortion during the printing-scanning process, improving the success rate of extracting implicit watermarks under different printers and paper types.

[0074] The generation and embedding of the visual watermark are consistent with that in Example 1, except that "font matching" is added in the aesthetic design stage—that is, the font of the visual text is consistent with the font of the page number characters, which further reduces the impact on the reading experience.

[0075] Optimized implementation of dual verification: This embodiment optimizes device compatibility and piracy monitoring capabilities in accurate verification, supplements the specific processing logic of the mobile application and the implementation of the crowdsourcing verification mechanism, corresponding to the technical features of "mobile application integrating image processing and decoding functions" and "supporting crowdsourcing verification nodes to upload anonymous verification data" in the claims.

[0076] Image processing optimization for the mobile application: Specifically, after acquiring the page number image, the mobile application adds an "automatic page number area cropping" function. The application uses image recognition algorithms to identify the header, footer, or sidebar area where the page number is located, automatically cropping away the main text area of ​​the page, retaining only the page number and a small amount of surrounding background, reducing interference from non-page number areas for watermark extraction. After cropping, the application performs "adaptive noise reduction" on the page number image—adjusting the noise reduction intensity according to the image's blur level. When the blur level is high, noise reduction is enhanced to avoid noise affecting watermark extraction; when the blur level is low, noise reduction is weakened to retain more watermark details.

[0077] In the watermark extraction stage, the application employs a "multi-region extraction and comparison" strategy: the cropped page number image is divided into 3-5 local regions, and watermark information is extracted from each region. If the watermark information extracted from multiple regions is consistent, the extraction result is considered valid; if discrepancies exist, image processing and extraction are repeated. This strategy aims to reduce verification errors caused by the failure of extraction from a single region and improve the verification success rate of the mobile application in non-ideal shooting environments.

[0078] Implementation of the crowdsourcing verification mechanism: Specifically, the central verification platform adds a "crowdsourcing verification data module" to its existing functions. After a user completes precise verification through a mobile application, the application will ask the user whether they agree to "anonymously upload the verification results." If the user agrees, the application only uploads three types of information: the book's unique identifier, the verification time, and the verification result, without including any buyer's private data. After receiving this anonymous data, the crowdsourcing module of the central verification platform categorizes and summarizes it according to the book's unique identifier, and calculates the number of verifications and the distribution of results for the same book's unique identifier.

[0079] When the percentage of "suspected piracy" verification results for a book's unique identifier exceeds a preset threshold, the central verification platform will send a "potential piracy warning" to the copyright holder, providing the verification time and geographical distribution of the book, forming an initial trajectory of piracy circulation. The copyright holder can then use this warning information to further investigate and pinpoint the source of the piracy.

[0080] The purpose of this crowdsourcing mechanism is to form a distributed monitoring network by utilizing the verification behavior of a large number of users. Compared with the single verification by the copyright holder, it can discover clues of piracy circulation more quickly and extensively, and improve the timeliness and coverage of piracy monitoring.

[0081] Optimized implementation of watermark lifecycle management: This embodiment supplements the watermark lifecycle processing in the context of book transfer, improves the technical features of "watermark lifecycle management" in the claims, and adapts to the actual needs of second-hand book circulation.

[0082] Specifically, the central verification platform adds an "identity identifier unbinding and rebinding function." When a buyer needs to transfer a book, they can submit a "transfer application" through the official channels of the central verification platform, providing the book's unique identifier and personal identity verification information. After the central verification platform approves the application, it will mark the original buyer's identity identifier as "transferred" and generate a temporary "transfer verification code."

[0083] After acquiring the book, the new buyer scans the page watermark using a mobile application, enters a temporary transfer verification code, and upon successful verification by the central verification platform, re-binds the new buyer's identity identifier with the book's unique identifier and updates the watermark's activation status. The original buyer's identity identifier is retained in the platform's database but marked as "not the current holder" to prevent misidentification of the original buyer as the source of the pirated book during subsequent piracy tracing.

[0084] The purpose of this optimization is to support the legal circulation of second-hand books without compromising the uniqueness of the watermark, thus solving the problem in Implementation Example 1 where "the original buyer may be misidentified after the book is transferred". At the same time, it ensures that second-hand books are still within the watermark monitoring range, preventing the loss of traceability after the second-hand books are illegally copied.

[0085] Compared to Embodiment 1, this embodiment further enhances the practicality and adaptability of the technical solution by optimizing the identity identifier generation method, implicit watermark embedding strategy, verification device compatibility, and lifecycle management. Specifically, the expansion of identity identifier sources enables it to adapt to offline purchase scenarios without accounts; the adaptive embedding optimization of implicit watermarks improves robustness under different printing processes; the crowdsourced verification mechanism enhances the timeliness of piracy monitoring; and the lifecycle management in transfer scenarios supports the circulation of second-hand books and avoids misjudgments.

[0086] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A dual-verification driven method for preventing book page number watermarking theft, characterized in that, Includes the following steps: Step 1: Watermark Information Generation: Calculate the hash value based on the page number text content and combine it with the buyer's identity identifier to generate dynamic watermark information, wherein the dynamic watermark information is unique for each page and each book; Step 2, Watermark Embedding: The dynamic watermark information is embedded into the book page number, including generating a visible watermark and an implicit watermark. The visible watermark contains part of the plaintext of the user's identity identifier or a specific code and is placed in a semi-transparent manner in the header, footer, or sidebar area of ​​the page number. The implicit watermark contains complete dynamic watermark information and is encoded into the character edges, stroke connections, or microstructure of the background texture of the page number through a lightweight embedding algorithm. Step 3, Dual Verification: This includes rapid verification and precise verification. Rapid verification makes a preliminary judgment on authenticity by directly observing the presence and content of the visible watermark. Precise verification obtains page number images through a scanning device and extracts the implicit watermark to perform content integrity verification and piracy tracing. Content integrity verification is achieved by comparing the extracted hash value with the real-time calculated hash value of the page number text content. Piracy tracing is achieved by querying the user identity identifier and record information extracted from the central verification platform. The implicit watermark embedding uses a lightweight embedding algorithm optimized for binary text images. This algorithm analyzes the binary image of the page number, selects character outline edges, stroke gaps, or background dot density as embedding regions, and encodes watermark information by fine-tuning pixel values ​​or texture changes. The embedding process includes error-correcting encoding of the watermark data, which uses Reed-Solomon coding. The encoding process is defined by the following mathematical expression: ； in, This represents the codeword polynomial generated after encoding. This represents a data polynomial composed of the original watermark data. This represents the total number of symbols in a Reed-Solomon codeword. The number of signs representing the data portion. Represents the generator polynomial of the Reed-Solomon code. The shift operator represents the power operation of a data polynomial, with the symbol... The symbol represents polynomial multiplication, and the symbol represents modular operation. In step three, rapid verification and precise verification work together. The rapid verification is completed by checking the visible watermark with the human eye in a well-lit environment. Precise verification requires the use of a scanning device or mobile application to obtain the page number image and extracting and analyzing the implicit watermark using a dedicated algorithm. The verification results of the two are cross-checked. If the visible watermark and the implicit watermark information do not match, it is determined to be a counterfeit.

2. The book page number watermarking anti-theft method driven by dual verification according to claim 1, characterized in that, In step one, the dynamic watermark information is composed of the hash value of the page number text content and the encrypted purchaser's identity identifier. The hash value is calculated using the secure hash algorithm SHA-256, and its mathematical expression is: ； in, This represents the output 256-bit hash value. The input message represents the page number text content. The buyer's identity identifier is an encrypted digest generated based on the user's order number or account. The encryption is completed in a central database using the AES encryption algorithm.

3. The book page number watermarking anti-theft method driven by dual verification according to claim 1, characterized in that, The visual watermark generation involves combining partial information of the user's identity identifier with copyright symbols and the book's unique identifier to form visual text or graphics. The visual text or graphics undergo semi-transparency processing and aesthetic design to adapt to the page layout in a way that does not affect normal reading. Furthermore, the visual watermark is directly superimposed onto the page layout file during the printing process to form a warning message that can be recognized by the human eye.

4. The book page number watermarking anti-theft method driven by dual verification according to claim 1, characterized in that, The content integrity verification is performed in the precise verification step, which includes obtaining the hash value of the page number text content from the extracted implicit watermark, and simultaneously calculating the hash value of the current page number text content in real time by scanning the image. If the two hash values ​​are inconsistent, it is determined that the page number content has been tampered with. The real-time calculation uses the same hash algorithm as the watermark information generation step.

5. The book page number watermarking anti-theft method driven by dual verification according to claim 1, characterized in that, The piracy tracing process is performed in the precise verification step, which includes decrypting the user identity identifier from the extracted implicit watermark and sending the identifier to the central verification platform for querying. The central verification platform returns the purchaser information corresponding to the identifier, thereby locating the original source of the pirated books. The query process is completed under the premise of privacy authorization, and the central verification platform records the binding status between the unique identifier of the book and the user identity identifier.

6. The book page number watermarking anti-theft method driven by dual verification according to claim 1, characterized in that, In step two, the embedding position of the implicit watermark is adaptively selected based on the local features of the page number, including analyzing the text layout and character structure, and preferentially making micro-adjustments at the character edges or stroke connections. The adjustment is achieved by modifying pixels or introducing texture changes, and the adjustment range is controlled within the range imperceptible to the human eye.

7. The book page number watermarking anti-theft method driven by dual verification according to claim 1, characterized in that, The method also includes step four, watermark lifecycle management, in which the central verification platform maintains the binding relationship between the unique identifier of the book and the user's identity identifier, and activates the status after the book is sold. It supports a remote invalidation mechanism. When a book is reported lost, the platform will blacklist the corresponding identifier and prompt the abnormal status during the verification process.

8. The book page number watermarking anti-theft method driven by dual verification according to claim 1, characterized in that, The scanning device used in step three includes a regular scanner or a mobile phone with a macro lens. The mobile application integrates image processing and decoding functions to automatically extract implicit watermarks and execute verification logic. The central verification platform is a cloud database system that provides application programming interfaces for verification queries and supports crowdsourced verification nodes to upload anonymous verification data to monitor the circulation path of pirated goods.

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