Digital watermark encryption and traceability method and system based on national cryptographic algorithm

Abstract

The invention provides a digital watermark encryption and traceability method and system based on a national cryptographic algorithm, and the method comprises the steps: carrying out the hash operation of original data, and obtaining a hash value; encrypting the hash value to obtain an encrypted hash value; generating digital watermark information according to a preset rule; wherein the digital watermark information comprises the encrypted hash value and traceability information; encrypting the digital watermark information based on a secret key generated by a national cryptographic algorithm to obtain an encrypted digital watermark; based on the original data, constructing a fractal feature matrix based on a fractal theory; embedding the encrypted digital watermark into the fractal feature matrix to obtain a fractal feature matrix in which the watermark is embedded; and carrying out reverse fractal reconstruction on the fractal feature matrix embedded with the watermark, and recovering target data with the digital watermark. According to the invention, the integrity and confidentiality of the watermark are guaranteed, and the watermark is not liable to be interfered and damaged by the outside.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a digital watermark encryption and source tracing method and system based on a national secret algorithm. Background Art

[0002] With the rapid development of information technology, the importance of data in various fields has become increasingly prominent, and digital watermarking plays a key role in data traceability. For digital watermarking technology, existing methods have many problems in the process of watermark embedding and extraction.

[0003] On the one hand, the embedding of watermarks often has a significant impact on the quality of the original data and reduces the availability of the data; on the other hand, after being subjected to common data processing operations such as noise interference, data compression, and malicious tampering, the integrity and readability of the watermark are difficult to guarantee, making data traceability difficult. Summary of the invention

[0004] The main purpose of the present invention is to provide a digital watermark encryption and traceability method and system based on the national secret algorithm, aiming to overcome the defects of the current watermark embedding that affects the quality of the original data and the difficulty in ensuring the integrity and readability of the watermark.

[0005] To achieve the above purpose, the present invention provides a digital watermark encryption and source tracing method based on a national secret algorithm, comprising the following steps: Performing a hash operation on the original data to obtain a hash value; encrypting the hash value to obtain an encrypted hash value; Generate digital watermark information according to preset rules; wherein the digital watermark information includes the encrypted hash value and traceability information; Encrypting the digital watermark information based on a key generated by a national secret algorithm to obtain an encrypted digital watermark; Based on the original data, a fractal feature matrix based on fractal theory is constructed; the encrypted digital watermark is embedded in the fractal feature matrix to obtain a fractal feature matrix after embedding the watermark; The fractal feature matrix after embedding the watermark is subjected to inverse fractal reconstruction to restore the target data with the digital watermark.

[0006] Furthermore, when tracing is required, the target data is reconstructed into a fractal feature matrix and the encrypted digital watermark is extracted. After the encrypted digital watermark is decrypted, the encrypted hash value is compared with the hash value recalculated from the original data, and combined with the tracing information, data tracing is achieved.

[0007] Furthermore, based on the original data, a fractal feature matrix based on fractal theory is constructed, including: Analyze the original data based on the principal component analysis method to extract the main characteristic components of the original data; Simulating the fractal characteristics of the main characteristic components based on a fractal Brownian motion model to generate a fractal characteristic pattern; According to the fractal feature pattern, a fractal feature matrix is ​​constructed.

[0008] Furthermore, the fractal feature matrix embedded with the watermark is subjected to inverse fractal reconstruction to restore the target data with the digital watermark, including: Using fractal feature extraction algorithm to locate the watermark information in the fractal feature matrix after embedding the watermark, and extracting the fractal feature matrix and the embedding position and feature data of the recorded watermark information; Based on the reverse generation algorithm, the extracted fractal feature data is restored, and the original structure of the fractal feature data is gradually restored to obtain the restored data; Based on the embedding position and characteristic data of the watermark information, the watermark information is embedded into the restored data to obtain target data with a digital watermark.

[0009] Furthermore, the step of encrypting the hash value to obtain an encrypted hash value includes: Obtain the traceability information and feature information of the original data; Generate an encryption key based on the traceability information and feature information; The hash value is encrypted based on the encryption key to obtain an encrypted hash value.

[0010] Further, generating an encryption key based on the traceability information and the characteristic information includes: Extracting the event sequence and user path in the traceability information, and converting the event sequence into a character sequence; Mapping the user path into a set of spatial coordinate points, and fitting and generating a curve based on the set of spatial coordinate points; Extracting multiple feature values ​​from the feature information, performing simulation operations on the feature values ​​based on the curve, and inserting the operation results into the character sequence according to a preset rule to obtain a fused sequence; extracting features of the curve, and deforming the fused sequence based on the features of the curve to obtain a deformed sequence; The deformation sequence is encoded to generate the encryption key.

[0011] Further, generating an encryption key based on the traceability information and the characteristic information includes: Encoding the traceability information to obtain encoded information; Add the characters in the coding information to the data table one by one in order to obtain a coding data table; Based on the feature information, a closed figure is generated; and the generated closed figure is superimposed on the coded data table; In the coding data table, the characters inside the closed figure are exchanged with the characters outside the closed figure one by one until all the characters inside the closed figure are exchanged, thereby obtaining a changed coding data table; Obtain two key feature points on the closed figure and connect them into a straight line; obtain a character having a preset position relationship with the straight line in the change coding data table as a target character; The target characters are combined in sequence to obtain the encryption key.

[0012] The present invention also provides a digital watermark encryption and source tracing system based on a national secret algorithm, comprising: A hash unit is used to perform a hash operation on the original data to obtain a hash value; and encrypt the hash value to obtain an encrypted hash value; A generating unit, configured to generate digital watermark information according to a preset rule; wherein the digital watermark information includes the encrypted hash value and traceability information; An encryption unit, used to encrypt the digital watermark information based on a key generated by a national secret algorithm to obtain an encrypted digital watermark; An embedding unit is used to construct a fractal feature matrix based on fractal theory based on the original data; embed the encrypted digital watermark into the fractal feature matrix to obtain the fractal feature matrix after embedding the watermark; The reconstruction unit is used to perform inverse fractal reconstruction on the fractal feature matrix after embedding the watermark, so as to restore the target data with the digital watermark.

[0013] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0014] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0015] The digital watermark encryption and traceability method and system based on the national secret algorithm provided by the present invention include: performing a hash operation on the original data to obtain a hash value; encrypting the hash value to obtain an encrypted hash value; generating digital watermark information according to a preset rule; wherein the digital watermark information includes the encrypted hash value and traceability information; encrypting the digital watermark information based on the key generated by the national secret algorithm to obtain an encrypted digital watermark; constructing a fractal feature matrix based on fractal theory based on the original data; embedding the encrypted digital watermark into the fractal feature matrix to obtain a fractal feature matrix after embedding the watermark; performing inverse fractal reconstruction on the fractal feature matrix after embedding the watermark to restore the target data with the digital watermark. In the present invention, the digital watermark information is encrypted based on the key generated by the national secret algorithm, which can prevent the watermark information from being tampered with or leaked during transmission and storage, and ensure the integrity and confidentiality of the watermark. Embedding the encrypted digital watermark into the fractal feature matrix can embed the watermark without significantly affecting the quality of the original data. The fractal feature matrix can provide protection for the watermark, making it less susceptible to external interference and damage, thereby ensuring the quality of the watermark after embedding. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the steps of a digital watermark encryption and source tracing method based on a national secret algorithm in one embodiment of the present invention; Figure 2 This is a structural block diagram of a digital watermark encryption and source tracing system based on a national secret algorithm in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0017] The implementation, functional features and advantages of the present invention will be further described in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] Reference Figure 1 In one embodiment of the present invention, a digital watermark encryption and source tracing method based on a national secret algorithm is provided, comprising the following steps: Step S1, performing a hash operation on the original data to obtain a hash value; encrypting the hash value to obtain an encrypted hash value; Step S2, generating digital watermark information according to preset rules; wherein the digital watermark information includes the encrypted hash value and traceability information; Step S3, encrypting the digital watermark information based on the key generated by the national secret algorithm to obtain an encrypted digital watermark; Step S4, constructing a fractal feature matrix based on fractal theory based on the original data; embedding the encrypted digital watermark into the fractal feature matrix to obtain a fractal feature matrix after embedding the watermark; Step S5, performing inverse fractal reconstruction on the watermarked fractal feature matrix to restore the target data with the digital watermark.

[0020] In this embodiment, as described in step S1 above, a hash function operation is performed on the original data. The hash function has characteristics such as unidirectionality and avalanche effect, and can compress the original data of any length into a hash value of fixed length. The purpose of this step is to extract the feature summary of the original data, so that even if the original data has a slight change, its hash value will change significantly, thereby providing a basis for subsequent data integrity verification. For example, in financial transaction data, hash operations can quickly determine whether the data has been tampered with.

[0021] The obtained hash value is encrypted using an encryption algorithm and converted into an encrypted hash value. This is to further protect the characteristic information of the original data, prevent the hash value from being obtained and tampered with without authorization, and enhance data security. For example, in some medical or scientific research data scenarios with high requirements for data privacy, encrypted hash values ​​can ensure the confidentiality of data characteristics.

[0022] As described in step S2 above, according to the preset rules, the encrypted hash value obtained in step S1 is integrated with the traceability information (such as splicing combination) to generate digital watermark information. The traceability information may include key information such as the source, creation time, and modification record of the data. This information combined with the encrypted hash value enables the digital watermark to not only reflect the integrity of the data, but also trace the source and historical operations of the data, providing strong support for copyright protection and responsibility tracing of the data. For example, in the copyright protection of digital media works, the traceability information can clarify the creator, publishing platform, and dissemination path of the work.

[0023] As described in step S3 above, a specific key is generated by using the national secret algorithm to encrypt the digital watermark information to obtain an encrypted digital watermark. The national secret algorithm has high-intensity encryption performance and can resist various forms of attacks, ensuring the security of the digital watermark during embedding and transmission, preventing the watermark information from being stolen or tampered with, and ensuring that only authorized parties can obtain and interpret the watermark information, thereby effectively protecting the copyright and authenticity of the data.

[0024] As described in step S4 above, based on the intrinsic characteristics of the original data, a fractal feature matrix is ​​constructed using fractal theory. Fractal theory can well describe the self-similarity and irregularity in nature and complex systems. By analyzing and processing the original data, its fractal features are extracted and constructed into a matrix form. The matrix can provide a stable and suitable carrier for the embedding of digital watermarks while retaining the key features of the original data. For example, in image data, the fractal feature matrix can capture the texture, shape and other features of the image, so that the embedding of the watermark will not have a significant impact on the visual effect of the image.

[0025] The encrypted digital watermark obtained in the above step S3 is embedded into the constructed fractal feature matrix to obtain the fractal feature matrix after embedding the watermark. This embedding method utilizes the characteristics of the fractal feature matrix, so that the watermark can be closely integrated with the characteristics of the original data, which not only improves the concealment of the watermark, but also enhances its anti-attack ability. Because the local changes of the fractal feature matrix will not affect the overall fractal characteristics, even if the original data is disturbed or processed to a certain extent, the watermark can still be well retained in the data.

[0026] As described in step S5 above, the fractal feature matrix embedded with the watermark is reversely operated, and the target data with the digital watermark is gradually restored according to the reconstruction algorithm of the fractal theory. This process is the reverse process of step S4, which is realized through precise mathematical calculations and algorithms, and ensures the integrity and readability of the digital watermark while restoring the original data. When it is necessary to verify the authenticity of the data, trace the source of the data or protect the copyright, the digital watermark information can be accurately extracted and interpreted, achieving the ultimate goal of the entire digital watermark encryption and traceability method, and providing reliable technical guarantee for the full life cycle management of data.

[0027] In this embodiment, the digital watermark information is encrypted based on the key generated by the national secret algorithm, which can prevent the watermark information from being tampered with or leaked during transmission and storage, thereby ensuring the integrity and confidentiality of the watermark. The encrypted digital watermark is embedded in the fractal feature matrix, so that the watermark can be embedded without significantly affecting the quality of the original data, and the fractal feature matrix can provide protection for the watermark, making it less susceptible to external interference and damage, thereby ensuring the quality of the watermark after embedding.

[0028] In one embodiment, when tracing is required, the target data is reconstructed into a fractal feature matrix and an encrypted digital watermark is extracted. After the encrypted digital watermark is decrypted, the encrypted hash value is compared with the hash value recalculated from the original data, and combined with the tracing information, data tracing is achieved.

[0029] In this embodiment, when faced with the need for data tracing, the fractal feature matrix is ​​first reconstructed based on the target data. This process relies on the fractal theory and algorithm originally used to construct the fractal feature matrix. By analyzing and extracting the features of the target data, a fractal feature matrix with similar structure and features to the original data is regenerated. Due to the self-similarity and stability of fractal theory, even if the target data undergoes some conventional data processing operations such as compression, format conversion, etc. during storage, transmission or use, the fractal feature matrix associated with the original data can still be restored to a large extent, thereby laying the foundation for the subsequent accurate extraction of encrypted digital watermarks.

[0030] Then, the encrypted digital watermark is extracted from the reconstructed fractal feature matrix. This step utilizes the specific technology and rules used in watermark embedding, and accurately separates the encrypted digital watermark by analyzing and processing the fractal feature matrix, ensuring its integrity and accuracy, and providing a reliable data source for subsequent decryption and tracing operations.

[0031] The extracted encrypted digital watermark is decrypted using the decryption algorithm corresponding to the encryption process to obtain the encrypted hash value and traceability information contained therein. The decryption algorithm must match the previous encryption algorithm and be safe and reliable to ensure that the encrypted hash value and traceability information can be accurately restored to avoid data loss or misinterpretation during the decryption process.

[0032] At the same time, the original data is re-hashed to obtain a new hash value. The encrypted hash value obtained by decryption is compared with the recalculated hash value. Hash value comparison is a key link in data integrity verification. Due to the characteristics of the hash function, as long as the original data is unauthorizedly modified at any stage, the two hash values ​​will be inconsistent. Through this comparison, it is possible to quickly determine whether the original data has been tampered with, providing a preliminary verification basis for the authenticity and reliability of the data.

[0033] In addition to hash value comparison, traceability information is also an important part of data traceability. Detailed records such as data source, creation time, operation history, etc. recorded in the traceability information provide key clues for tracking the flow path and responsibility of data. For example, in a complex supply chain data management system, traceability information can be used to clarify the information of every link from the raw material supplier to the final product manufacturer, including the data generator, modifier, modification time, and modification reason. When problems are found in the data, the link and person responsible for the problem can be quickly located to achieve accurate data traceability, effectively ensure the security and traceability of the data, safeguard the legitimate rights and interests of all participants, and also provide strong support for the stable operation of the entire data ecosystem.

[0034] In one embodiment, based on the original data, a fractal feature matrix based on fractal theory is constructed, including: Analyze the original data based on the principal component analysis method to extract the main characteristic components of the original data; Simulating the fractal characteristics of the main characteristic components based on a fractal Brownian motion model to generate a fractal characteristic pattern; According to the fractal feature pattern, a fractal feature matrix is ​​constructed.

[0035] In this embodiment, the principal component analysis (PCA) is a commonly used multivariate statistical analysis method, and its core purpose is to transform multiple variables that may be correlated in the original data into a set of mutually orthogonal (linearly independent) new variables, namely, principal components, through linear transformation. These principal components are arranged from large to small according to the variance, and the principal components in the front can carry most of the information in the original data, namely, the main characteristic components.

[0036] For example, suppose there is a set of image data as raw data, which may contain information of multiple dimensions such as color and brightness of many pixels, and there is often a certain correlation between these dimensions. Through the PCA method, the above complex and related dimensional information can be integrated to extract several principal components that best represent the characteristics of this set of image data, such as the overall light and dark contrast of the image, the main color distribution tendency, etc., so as to achieve the effect of dimensionality reduction and extraction of key features.

[0037] The above raw data are often complex and multi-dimensional. Directly using fractal theory to process them will face problems such as excessive calculation and difficulty in accurately capturing fractal features. However, using PCA to extract the main characteristic components first can remove redundant information in the raw data and focus on the most representative parts, so that subsequent operations based on fractal theory can be carried out more efficiently and accurately around key features, providing a more concise data foundation that can highlight core features for constructing a fractal feature matrix.

[0038] The above-mentioned fractal Brownian motion model is a mathematical model that can effectively describe natural phenomena and complex systems with fractal characteristics. Its characteristics are that it can generate random processes or data sequences with fractal characteristics such as self-similarity, long-range correlation and statistical self-affineness.

[0039] In this model, by adjusting specific parameters (such as the Hurst exponent, etc.), the specific manifestations of the generated fractal features can be controlled, such as different degrees of roughness, self-similarity, etc. For example, when simulating topographic data, the fractal Brownian motion model can be used to generate a virtual terrain surface with fractal features that is similar to the actual terrain, which shows similar undulating shapes at different scales, which is the embodiment of fractal features.

[0040] After the main characteristic components of the original data have been extracted, these components still need to be further transformed into patterns with clear fractal characteristics that meet the requirements of fractal theory, so the fractal Brownian motion model is introduced. The main characteristic components are taken as input, and the relevant parameters of the fractal Brownian motion model are reasonably set according to the characteristics of the data and the expected fractal characteristics, so that the fractal characteristic patterns generated by the model can match these main characteristic components, and simulate the characteristics of the original data at the fractal level as realistically as possible, such as simulating the similar structure and complexity of the data at different scales, so as to provide a specific pattern basis with fractal characteristics for generating fractal feature matrices.

[0041] Once the fractal characteristic pattern is obtained through the fractal Brownian motion model, it needs to be converted into a matrix form for storage and subsequent application. This process usually involves quantizing, discretizing, and arranging the fractal characteristic pattern according to a certain spatial or logical layout.

[0042] For example, for the fractal feature pattern corresponding to two-dimensional image data, the relevant quantitative values ​​of the fractal features can be assigned to the corresponding matrix element positions according to the pixel coordinate positions of the image to form a two-dimensional fractal feature matrix, in which each element of the matrix carries the fractal feature information at the corresponding position, and the entire matrix completely describes the overall fractal characteristics of the image data.

[0043] The constructed fractal feature matrix plays a key role in the entire digital watermark encryption and traceability method. It not only carries the fractal features of the original data after extraction and simulation, but also provides a suitable carrier for the subsequent embedding of digital watermarks. After the digital watermark is embedded in this matrix, it can be better integrated with the original data with the help of the stability and self-similarity of the fractal feature matrix. In the face of situations such as data compression and noise interference, it can still ensure the stability and extractability of the watermark. At the same time, it also provides an accurate basis and basic data structure for subsequent reverse fractal reconstruction and data traceability operations.

[0044] In one embodiment, the fractal feature matrix embedded with the watermark is subjected to inverse fractal reconstruction to restore the target data with the digital watermark, including: Using fractal feature extraction algorithm to locate the watermark information in the fractal feature matrix after embedding the watermark, and extracting the fractal feature matrix and the embedding position and feature data of the recorded watermark information; Based on the reverse generation algorithm, the extracted fractal feature data is restored, and the original structure of the fractal feature data is gradually restored to obtain the restored data; Based on the embedding position and characteristic data of the watermark information, the watermark information is embedded into the restored data to obtain target data with a digital watermark.

[0045] In this embodiment, the fractal feature extraction algorithm aims to identify and extract feature information related to fractals from complex data structures. In this embodiment, for the fractal feature matrix after embedding the watermark, the algorithm can accurately locate the watermark information therein based on the unique identification and rules of the fractal features. Since the fractal feature matrix itself has specific fractal properties such as self-similarity and scale invariance, the algorithm will use these properties and the specific rules and patterns followed when embedding the watermark, and accurately determine the location of the watermark information by analyzing the relationship between the matrix elements, the distribution of values, etc.

[0046] For example, when embedding the watermark into the fractal feature matrix, the fractal dimension and texture features of some local areas may be changed. The fractal feature extraction algorithm can keenly capture these subtle changes, thereby locking the specific location of the watermark. At the same time, in the process of locating the watermark information, the entire fractal feature matrix will also be completely extracted. This is because the subsequent restoration operation needs to be based on the complete fractal feature data, and the embedded position of the watermark information and related feature data will be recorded in detail, such as the fractal parameters of the area where the watermark is located, the associated features with the surrounding elements, etc. These recorded data provide a key reference for the subsequent accurate restoration of data and re-embedding of watermarks.

[0047] This step is the starting point of reverse fractal reconstruction and the basis for the smooth implementation of subsequent operations. Accurately locating and extracting watermark information and related fractal feature matrix data can ensure that the entire reverse reconstruction process will not lose key information, avoid restoration failure or watermark information confusion due to the inability to determine the watermark position or the lack of necessary fractal feature data, and provide precise guidance and reliable data foundation for subsequent restoration and re-embedding operations, ensuring that the target data with digital watermarks can be accurately restored.

[0048] The above reverse generation algorithm corresponds to the forward generation algorithm used in the previous construction of the fractal feature matrix. It is based on the reversible principle and mathematical logic in fractal theory. After obtaining the extracted fractal feature data, the algorithm will operate according to the pre-set steps and rules that are opposite to the forward construction process.

[0049] For example, when constructing a fractal feature matrix in a forward direction, it may involve multi-scale decomposition of the original data, adjusting parameters according to the fractal model, and other operations to generate a specific fractal structure. The reverse generation algorithm will start from the currently extracted fractal feature data, and through reverse parameter adjustment, inverse scale transformation and other operations, gradually eliminate the fractal feature modifications added in the construction process, and restore the fractal feature data to a state closer to the original data, so that its original structure can be restored. This process is a step-by-step iterative and fine restoration process, which requires strict adherence to the mathematical relationship of fractal theory to ensure that each step of the restoration operation can approximate the fractal structure of the original data to the greatest extent possible, and finally obtain the restored data.

[0050] This step plays a core bridge role in the entire reverse fractal reconstruction process. The restoration of fractal feature data through the reverse generation algorithm can eliminate the changes in the fractal structure of the original data caused by the watermark embedding process, so that the data can be restored to a relatively pure form close to the original state, creating conditions for the subsequent accurate re-embedding of the watermark and the restoration of the target data with complete and correct watermark information. If this restoration operation is inaccurate or incomplete, the subsequent re-embedded watermark may not be well adapted to the original data, thereby affecting the data traceability and the effectiveness of the entire digital watermark encryption and traceability method.

[0051] After the restored data has been obtained and the embedding position and feature data of the watermark information are clearly known, the watermark information is re-embedded into the restored data. This process is not a simple repetition of the previous embedding operation, but a precise embedding based on the precise position and feature data previously recorded.

[0052] For example, the watermark information elements will be accurately placed at the corresponding positions of the restored data according to the previously recorded specific coordinate positions in the fractal feature matrix, the way of association with the surrounding elements, etc. At the same time, the embedding strength, method and other details should be adjusted with reference to the previously recorded feature data to ensure that the re-embedded watermark and the restored data can be perfectly integrated, which will not have a negative impact on the structure and quality of the restored data, and can ensure that the watermark information is complete and can be accurately extracted, so that the target data with digital watermarks is consistent with the original data with embedded watermarks in structure and function, thus achieving the goal of the entire reverse fractal reconstruction, and providing accurate and reliable data support for subsequent data verification, traceability and other operations. By accurately re-embedding the watermark information into the restored data, it can be ensured that after the complex process of reverse fractal reconstruction, the target data still carries a complete and effective digital watermark, so that the data can be used in subsequent application scenarios, whether it is copyright verification, data traceability or other related operations, to achieve the corresponding functions based on the information provided by the digital watermark, ensuring the reliability and practicality of the digital watermark encryption and traceability method based on fractal theory.

[0053] In one embodiment, the step of encrypting the hash value to obtain an encrypted hash value includes: Obtain the traceability information and feature information of the original data; Generate an encryption key based on the traceability information and feature information; The hash value is encrypted based on the encryption key to obtain an encrypted hash value.

[0054] In this embodiment, the traceability information covers many key aspects related to the generation and circulation of original data, such as the source of the data, which specific device, system or user end the data comes from; the time when the data was generated, clarifying the time node when it was first generated, which is important for judging the timeliness of the data and tracking the data life cycle; and the operation records that the data has undergone, such as whether it has been modified, at which links it has been transmitted, and by whom under what authority, etc. Obtaining this traceability information usually requires the use of the logging function in the corresponding data management system, or marking and storing it at the source of data generation through a specific identification and recording mechanism to ensure that it can be completely and accurately extracted when needed later.

[0055] Feature information focuses on reflecting the inherent characteristics of the original data itself, and its extraction method varies depending on the data type. For numerical data, statistical features such as mean, variance, and distribution form may be calculated; for text data, word frequency, text structure, semantic features, etc. will be analyzed; for image data, important feature information such as color histogram, texture features, and shape features are important. The purpose of extracting this feature information is to characterize the original data from different dimensions, so that encryption keys closely related to it can be generated based on these unique features in the future, enhancing the pertinence and security of encryption.

[0056] The fusion of traceability information and feature information to generate encryption keys utilizes the uniqueness and comprehensiveness of these two types of information to improve the security and unpredictability of the key. Specifically, the traceability information may be formatted first, and its elements may be converted into a data sequence in a specific format according to certain coding rules, such as converting timestamps into binary codes and operation records into specific character sequences; the feature information will also be quantified and encoded so that it can participate in the unified key generation operation. Then, through a specific key generation algorithm, such as a hash function, a key expansion function in a symmetric encryption algorithm, or a public-private key generation mechanism in an asymmetric encryption algorithm, the processed traceability information and feature information are combined and calculated to generate the final encryption key. In this process, the traceability information ensures that the key is associated with the source and flow history of the data, and the feature information makes the key fit the characteristics of the original data itself. The combination of the two makes the generated encryption key highly unique and difficult to be guessed or cracked by external attackers through conventional means.

[0057] The encryption key is at the core of the entire encryption system, and it determines the security and reliability of encryption. By generating encryption keys based on traceability information and feature information, the subsequent encryption operations on hash values ​​can be more targeted. The key is generated closely around the various characteristics of the original data. Compared with universal and fixed keys, this key generated based on the data itself can more effectively protect the security of hash values ​​and related data in the face of various security threats, and build a solid security line for the entire digital watermark encryption and traceability method.

[0058] In one embodiment, generating an encryption key based on the traceability information and the feature information includes: Extracting the event sequence and user path in the traceability information, and converting the event sequence into a character sequence; Mapping the user path into a set of spatial coordinate points, and fitting and generating a curve based on the set of spatial coordinate points; Extracting multiple feature values ​​from the feature information, performing simulation operations on the feature values ​​based on the curve, and inserting the operation results into the character sequence according to a preset rule to obtain a fused sequence; extracting features of the curve, and deforming the fused sequence based on the features of the curve to obtain a deformed sequence; The deformation sequence is encoded to generate the encryption key.

[0059] In this embodiment, the event sequence in the traceability information records the order of a series of related events that occurred around the original data. For example, in a file management system, it contains the time sequence of operation events such as file creation, modification, sharing, and deletion. The user path reflects the access trajectory of different users when interacting with the original data, such as which entrance to enter the system, which functional modules to operate the data, etc. These two parts of information are extracted because they contain the key dynamic conditions of the data in the circulation process and can reflect the historical behavior characteristics of the data from different angles.

[0060] The purpose of converting the event sequence into a character sequence is to enable it to participate in the encryption key generation process in a unified form that is easy to process later. The specific conversion method can be to assign a specific character identifier to each different event type, and arrange these characters in sequence according to the order in which the events occur to form a character sequence. For example, "A" represents file creation and "B" represents file modification. If the event sequence is to create the file first and then modify it, then the converted character sequence is "AB". In this way, complex event logical relationships can be abstracted and represented in a simple character form, which is convenient for fusion and calculation with other information. It also helps to ensure the standardization and processability of information, so that the encryption key generated based on the character sequence can carry the key features of the event sequence in the traceability information.

[0061] User paths often involve access trajectories in different system interfaces, functional modules or virtual spaces. In order to make more intuitive and effective use of these path information, mapping them into a set of spatial coordinate points is an effective processing method. Different functional modules and interfaces in the system can be abstracted into coordinate points in two-dimensional or three-dimensional space. Each time a user enters from one module to another, the operation corresponds to the movement between the coordinate points. These coordinate points are recorded in sequence according to the order of user access, forming a set of spatial coordinate points. For example, in a software application with multiple pages and functional options, the homepage is set as the coordinate origin (0, 0), and each functional page entered by clicking is assigned a corresponding coordinate value according to certain rules. The path of users accessing these pages in sequence can be converted into a series of coordinate points. This mapping method can visualize the abstract user operation path, which is convenient for subsequent analysis and processing by mathematical methods.

[0062] Based on the obtained set of spatial coordinate points, a curve is generated using mathematical fitting methods (such as polynomial fitting, spline curve fitting, etc.). This curve can describe the trend and characteristics of the user path as a whole. It can capture the continuity and change characteristics of user operations, such as a simple straight-line access path or a complex path with multiple detours and jumps. Compared with a discrete set of coordinate points, the generated curve can better extract the regularity and integrity information in the user path, providing an element with geometric characteristics and dynamic characteristics for subsequent fusion with feature information and generation of encryption keys, so that encryption keys can be integrated into the unique pattern of user operation behavior, enhancing the personalization and security of the key.

[0063] Multiple eigenvalues ​​are extracted from the characteristic information of the original data. These eigenvalues ​​are quantitative descriptions of the intrinsic characteristics of the original data from different dimensions. For example, for image data, eigenvalues ​​such as color mean and contrast may be extracted, and for numerical data, statistical eigenvalues ​​such as mean, variance, and skewness may be extracted. They can accurately reflect the unique properties of the original data itself, so that the encryption key generation process not only relies on traceability information, but also closely combines the characteristics of the data itself, increases the correlation between the key and the original data, and further improves the pertinence and security of encryption.

[0064] Based on the previously generated curve, the extracted eigenvalues ​​are simulated and operated. This operation process aims to organically integrate the dynamic characteristics of the user path contained in the curve with the static characteristics of the original data represented by the eigenvalue. The simulation operation method can be determined according to the specific encryption design requirements. For example, the coordinate values ​​along the curve are weighted summed and multiplied with the eigenvalues, so that the eigenvalues ​​change accordingly in the dynamic environment constructed by the curve, and a new operation result that integrates the two information features is generated.

[0065] Then, these operation results are inserted into the character sequence according to the preset rules, such as inserting an operation result every few characters, or selectively inserting corresponding operation results according to the importance of the events represented by different characters in the character sequence, etc. Through such operations, a fusion sequence is obtained, which integrates the event sequence in the traceability information and the original data feature information after the curve simulation operation fusion.

[0066] The main purpose of extracting the features of the curve is to obtain more in-depth and representative information about the user path. These features include the curvature of the curve, the change in slope, the length of the curve, the coordinates of the starting and ending points, etc. The curvature of the curve can reflect the drastic degree of the user path's turning at certain stages, and the change in slope reflects the speed of the operation. These features describe the behavioral pattern characteristics of the user's interaction with the original data from different angles.

[0067] The purpose of deforming the fusion sequence based on the extracted curve features is to further increase the complexity and uniqueness of the fusion sequence on the basis of incorporating the curve features, making the encryption key generated in the end more difficult to crack or guess. For example, if the curvature of the curve is large in a certain section, some characters in the fusion sequence can be transformed by transposing, replacing, or adding specific identification characters; according to the change in the slope of the curve, it is decided to repeat, delete, and other processing of characters in certain positions in the fusion sequence. Through this deformation operation based on the curve features, the fusion sequence carries more information about the traceability information and the characteristics of the original data, laying the foundation for generating high-strength and high-security encryption keys.

[0068] Encoding the deformation sequence is to convert the complex sequence with specific structure and information obtained after multiple steps of processing into a binary format suitable for encryption keys or other formats that meet the requirements of the encryption algorithm. The encoding method can adopt common encoding methods, such as ASCII encoding to convert characters into corresponding binary codes, or use specially designed custom encoding rules adapted to this encryption scheme to uniformly encode various elements in the deformation sequence (including characters, inserted operation results, and logos added according to curve characteristics, etc.), and finally generate an encryption key that meets the encryption requirements. This step is the final link of the entire process of generating encryption keys based on traceability information and feature information. Through encoding, the information carefully integrated and processed in the previous steps is converted into an encryption key, so that it can be directly applied to subsequent encryption operations on key data such as hash values. The generated encryption key has extremely high security, uniqueness and unpredictability because it fully integrates the event sequence, user path and multi-dimensional characteristics of the original data in the traceability information, and has undergone complex operations, deformation and encoding processing, which provides a solid and reliable security guarantee for the entire digital watermark encryption and traceability method based on the national secret algorithm, and effectively protects the security and traceability of data at all links.

[0069] In one embodiment, generating an encryption key based on the traceability information and the feature information includes: Encoding the traceability information to obtain encoded information; Add the characters in the coding information to the data table one by one in order to obtain a coding data table; Based on the feature information, a closed figure is generated; and the generated closed figure is superimposed on the coded data table; In the coding data table, the characters inside the closed figure are exchanged with the characters outside the closed figure one by one until all the characters inside the closed figure are exchanged, thereby obtaining a changed coding data table; Obtain two key feature points on the closed figure and connect them into a straight line; obtain a character having a preset position relationship with the straight line in the change coding data table as a target character; The target characters are combined in sequence to obtain the encryption key.

[0070] In this embodiment, the traceability information usually includes various formats and types of content such as data source, operation records, timestamps, etc., which are complex and not conducive to directly participating in the subsequent encryption key generation operation. Encoding it means converting these diverse traceability information into a unified format that is easy for computers to process and recognize according to specific rules, such as converting it into a binary code or a coding sequence composed of a specific character set.

[0071] The purpose of adding the characters in the coded information to the data table one by one is to organize the coded content of the traceability information in a more structured and visual way. The data table can be in the form of a two-dimensional table, and the order of characters can be arranged in each row and column according to certain rules. For example, characters can be added row by row in chronological order, or characters can be placed column by column according to the classification of different types of traceability information.

[0072] The feature information of the original data describes the characteristics of the data itself from different dimensions. Generating closed figures based on these feature information is a way to visualize abstract data features. The specific generation method varies depending on the data type and features. For example, for image data with spatial distribution characteristics, a closed figure such as a polygon may be outlined using a geometric algorithm based on the outline of the object in the image, the color distribution area and other features; for numerical data, closed figures such as circles and ellipses may be drawn through mathematical functions based on the distribution range of its values, statistical laws, etc. to represent its characteristics. It is also possible to directly map a preset closed figure in the database based on the feature information.

[0073] The purpose of superimposing the generated closed graph onto the coded data table is to establish an intuitive connection between the data features and the traceability information, so that the characters in the coded data table can be processed in a targeted manner based on the positional relationship of the graph. This superposition operation creates a new fusion mode, allowing the traceability information and data features to be spatially correlated, so as to further mine and fuse the key information in both to generate a unique and secure encryption key. It also increases the complexity and uniqueness of the key generation process, which helps to improve the security of the final encryption key.

[0074] The character position exchange operation in the coding data table is based on the difference between the inner and outer areas divided by the closed graphics superimposed above, and the position exchange is used to realize the rearrangement and combination of the traceability information coding characters. By exchanging the characters inside the closed graphics with the characters outside one by one, the coded information originally arranged in sequence is disrupted and rearranged, and this rearrangement is carried out based on the closed graphics, an element related to the data characteristics, so that the information carried by the coding data table integrates the new logical relationship brought by the traceability information and data characteristics.

[0075] The changed coding data table is a new data structure obtained after the unique information fusion and rearrangement operations mentioned above. Compared with the original coding data table, it more deeply integrates the characteristics of traceability information and original data. The arrangement order of its internal characters contains more comprehensive and implicit information about the data source, operation and its own characteristics, providing more valuable materials for the subsequent extraction of key characters to generate encryption keys.

[0076] The key feature points on a closed figure are usually selected from those that can represent important geometric features of the figure or are closely related to data features, such as the vertices of a polygon, the center of a circle and the points at specific angles on the circumference of the circle, etc. The purpose of connecting these two key feature points to form a straight line is to determine a reference line with clear geometric meaning and representativeness on the figure. This straight line can divide different spatial areas or reflect specific directional attributes based on the characteristics of the figure, thereby providing a clear basis related to the graphic features for selecting target characters from the change coding data table.

[0077] The target characters are obtained according to the preset positional relationship between the straight line and the change coding data table (such as characters within a certain range from the straight line, characters on one side of the straight line, etc.), so that the final selected characters are not only associated with the traceability information after multiple rounds of processing, but also have a close spatial correspondence with the closed graphics generated based on data features and their key straight lines. These target characters carry the most core and critical comprehensive information in the entire encryption key generation process, preparing for the final combination to generate the encryption key. At the same time, this screening method based on graphic features and specific positional relationships further ensures the uniqueness and security of the encryption key.

[0078] Combine the selected target characters in a certain order to form the final encryption key. This order can be arranged according to the original order in the change coding data table, or according to other logical orders related to traceability information and data characteristics, to ensure that the combined key can completely and accurately carry the key information integrated and refined in the previous steps.

[0079] The generated encryption key fully combines the encoded content of the traceability information, the complex logic after graphic association and position exchange processing, and the target characters screened out based on the key features of the closed graphics. It has a high degree of complexity, uniqueness and unpredictability. It can provide strong security protection for data in subsequent digital watermark encryption, traceability and other related operations, effectively resist external illegal access and cracking attempts, and maintain the security and traceability of data.

[0080] In one embodiment, generating an encryption key based on the traceability information and the feature information includes: The key elements in the traceability information are extracted and converted into a character sequence, and the character sequence is error-corrected and encoded to obtain an error-correcting coding sequence.

[0081] Perform spectrum analysis on the characteristic information of the original data and extract key characteristic values; A two-dimensional matrix is ​​constructed using an error correction code sequence, and the structure of the two-dimensional matrix is ​​adjusted according to a key eigenvalue to obtain an adjusted matrix; Using quantum encryption, the elements in the adjustment matrix are quantum-state transformed and random key fragments are integrated to obtain an enhanced matrix; The enhanced matrix is ​​operated to generate the encryption key.

[0082] In this embodiment, key elements such as the order of events, operating subjects, and related equipment identification are first extracted from the traceability information and converted into a character sequence. This is to standardize the complex and diverse traceability information for subsequent processing. Then error correction coding is performed, such as using common cyclic redundancy check (CRC) and other error correction coding methods, which can effectively detect and correct errors that may occur in the character sequence during transmission, storage or processing, ensure the accuracy and integrity of the information, and finally obtain an error correction coding sequence, which provides reliable basic data for the subsequent construction of a two-dimensional matrix, and also ensures the stability and reliability of the traceability information in the entire encryption key generation process.

[0083] Perform spectrum analysis on the characteristic information of the original data, and use tools such as Fast Fourier Transform (FFT) to convert it from the time domain to the frequency domain, so as to clearly show the frequency distribution of the characteristic information. On this basis, extract key characteristic values, such as the energy value of a specific frequency band, the mean and variance of the frequency, etc. These characteristic values ​​can accurately summarize the unique properties of the original data in the frequency domain, and provide key data support for the subsequent fusion with traceability information, so that the encryption key can be closely combined with the inherent characteristics of the original data, and enhance the pertinence and security of encryption.

[0084] A two-dimensional matrix is ​​constructed based on the character sequence of the traceability information after error correction coding, for example, characters are filled row by row or column by column in order to form a matrix with a certain structure. Then, the matrix structure is adjusted according to the key eigenvalues ​​extracted from the feature information, such as changing the element distribution of certain rows and columns according to the size of the eigenvalue, merging or expanding the matrix elements in a specific area, etc., so that the matrix not only contains the traceability information, but also incorporates the feature information of the original data, becoming a carrier that integrates multiple key information, providing a more valuable data structure foundation for subsequent encryption operations, and improving the relevance and uniqueness of the encryption key and the data.

[0085] The adjusted matrix is ​​further processed using quantum encryption technology. Through quantum state transformation, such as using quantum gate operations to make matrix elements enter quantum superposition or entangled states, the randomness and unpredictability of the elements are increased, making it difficult for attackers to crack them through traditional mathematical methods. At the same time, the integration of random key fragments, such as random binary sequences generated by simulating the quantum key distribution process, and performing logical operations (such as XOR operations) with matrix elements further strengthens the confidentiality and security of the matrix, converting it into an enhanced matrix, so that the encryption key finally generated has higher security protection capabilities and effectively resists various potential security threats.

[0086] Perform specific mathematical operations on the enhanced matrix after quantum encryption enhancement, such as matrix multiplication and hash operations, to convert it into a fixed-length binary sequence with high randomness and complexity. This sequence is the encryption key generated in the end. This encryption key fully integrates the traceability information and the characteristic information of the original data, and has been enhanced by quantum encryption. It has extremely high security and uniqueness, and can provide strong security protection for data in the subsequent digital watermark encryption and traceability process, ensuring the confidentiality, integrity and traceability of the data, and effectively responding to various challenges faced in the modern complex and ever-changing network security environment.

[0087] Reference Figure 2 In one embodiment of the present invention, a digital watermark encryption and source tracing system based on a national secret algorithm is provided, including: A hash unit is used to perform a hash operation on the original data to obtain a hash value; and encrypt the hash value to obtain an encrypted hash value; A generating unit, configured to generate digital watermark information according to a preset rule; wherein the digital watermark information includes the encrypted hash value and traceability information; An encryption unit, used to encrypt the digital watermark information based on a key generated by a national secret algorithm to obtain an encrypted digital watermark; An embedding unit is used to construct a fractal feature matrix based on fractal theory based on the original data; embed the encrypted digital watermark into the fractal feature matrix to obtain the fractal feature matrix after embedding the watermark; The reconstruction unit is used to perform inverse fractal reconstruction on the fractal feature matrix after embedding the watermark, so as to restore the target data with the digital watermark.

[0088] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the description in the above method embodiment, which will not be repeated here.

[0089] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0090] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0091] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0092] In summary, the digital watermark encryption, traceability method and system based on the national secret algorithm provided in the embodiment of the present invention include: performing a hash operation on the original data to obtain a hash value; encrypting the hash value to obtain an encrypted hash value; generating digital watermark information according to preset rules; wherein the digital watermark information includes the encrypted hash value and traceability information; encrypting the digital watermark information based on the key generated by the national secret algorithm to obtain an encrypted digital watermark; constructing a fractal feature matrix based on fractal theory based on the original data; embedding the encrypted digital watermark into the fractal feature matrix to obtain a fractal feature matrix after embedding the watermark; performing inverse fractal reconstruction on the fractal feature matrix after embedding the watermark to restore the target data with the digital watermark. In the present invention, encrypting the digital watermark information based on the key generated by the national secret algorithm can prevent the watermark information from being tampered with or leaked during transmission and storage, thereby ensuring the integrity and confidentiality of the watermark. Embedding the encrypted digital watermark into the fractal feature matrix can embed the watermark without significantly affecting the quality of the original data. The fractal feature matrix can provide protection for the watermark, making it less susceptible to external interference and damage, thereby ensuring the quality of the watermark after embedding.

[0093] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0094] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0095] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A digital watermark encryption and source tracing method based on a national secret algorithm, characterized in that: The following steps are involved: Perform hash operation on the original data to obtain a hash value; Encrypting the hash value to obtain an encrypted hash value; Generate digital watermark information according to preset rules; wherein the digital watermark information includes the encrypted hash value and traceability information; Encrypting the digital watermark information based on a key generated by a national secret algorithm to obtain an encrypted digital watermark; Based on the original data, a fractal feature matrix based on fractal theory is constructed; the encrypted digital watermark is embedded in the fractal feature matrix to obtain a fractal feature matrix after embedding the watermark; The fractal feature matrix after embedding the watermark is subjected to inverse fractal reconstruction to restore the target data with the digital watermark.

2. The digital watermark encryption and source tracing method based on the national secret algorithm according to claim 1 is characterized in that: When tracing the source is required, the target data is reconstructed into a fractal feature matrix and the encrypted digital watermark is extracted. After the encrypted digital watermark is decrypted, the encrypted hash value is compared with the hash value recalculated from the original data, and combined with the tracing information, the data is traced.

3. The digital watermark encryption and source tracing method based on the national secret algorithm according to claim 1 is characterized in that: Based on the original data, a fractal feature matrix based on fractal theory is constructed, including: Analyze the original data based on the principal component analysis method to extract the main characteristic components of the original data; Simulating the fractal characteristics of the main characteristic components based on a fractal Brownian motion model to generate a fractal characteristic pattern; According to the fractal feature pattern, a fractal feature matrix is ​​constructed.

4. The digital watermark encryption and source tracing method based on the national secret algorithm according to claim 1 is characterized in that: Performing inverse fractal reconstruction on the watermarked fractal feature matrix to restore the target data with the digital watermark includes: Using fractal feature extraction algorithm to locate the watermark information in the fractal feature matrix after embedding the watermark, and extracting the fractal feature matrix and the embedding position and feature data of the recorded watermark information; Based on the reverse generation algorithm, the extracted fractal feature data is restored, and the original structure of the fractal feature data is gradually restored to obtain the restored data; Based on the embedding position and characteristic data of the watermark information, the watermark information is embedded into the restored data to obtain target data with a digital watermark.

5. The digital watermark encryption and source tracing method based on the national secret algorithm according to claim 1 is characterized in that: The step of encrypting the hash value to obtain an encrypted hash value comprises: Obtain the traceability information and feature information of the original data; Generate an encryption key based on the traceability information and feature information; The hash value is encrypted based on the encryption key to obtain an encrypted hash value.

6. The digital watermark encryption and source tracing method based on the national secret algorithm according to claim 5 is characterized in that: Generating an encryption key based on the traceability information and the characteristic information includes: Extracting the event sequence and user path in the traceability information, and converting the event sequence into a character sequence; Mapping the user path into a set of spatial coordinate points, and fitting and generating a curve based on the set of spatial coordinate points; Extracting multiple feature values ​​from the feature information, performing simulation operations on the feature values ​​based on the curve, and inserting the operation results into the character sequence according to a preset rule to obtain a fused sequence; extracting features of the curve, and deforming the fused sequence based on the features of the curve to obtain a deformed sequence; The deformation sequence is encoded to generate the encryption key.

7. The digital watermark encryption and source tracing method based on the national secret algorithm according to claim 5 is characterized in that: Generating an encryption key based on the traceability information and the characteristic information includes: Encoding the traceability information to obtain encoded information; Add the characters in the coding information to the data table one by one in order to obtain a coding data table; Based on the feature information, a closed figure is generated; and the generated closed figure is superimposed on the coded data table; In the coding data table, the characters inside the closed figure are exchanged with the characters outside the closed figure one by one until all the characters inside the closed figure are exchanged, thereby obtaining a changed coding data table; Obtain two key feature points on the closed figure and connect them into a straight line; obtain a character having a preset position relationship with the straight line in the change coding data table as a target character; The target characters are combined in sequence to obtain the encryption key.

8. A digital watermark encryption and traceability system based on national secret algorithm, characterized in that: include: A hash unit is used to perform a hash operation on the original data to obtain a hash value; Encrypting the hash value to obtain an encrypted hash value; A generating unit, configured to generate digital watermark information according to a preset rule; wherein the digital watermark information includes the encrypted hash value and traceability information; An encryption unit, used to encrypt the digital watermark information based on a key generated by a national secret algorithm to obtain an encrypted digital watermark; An embedding unit is used to construct a fractal feature matrix based on fractal theory based on the original data; embed the encrypted digital watermark into the fractal feature matrix to obtain the fractal feature matrix after embedding the watermark; The reconstruction unit is used to perform inverse fractal reconstruction on the fractal feature matrix after embedding the watermark, so as to restore the target data with the digital watermark.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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