553 results about "Watermark embedding" patented technology

The invention provides a multi-feature information hidden document integrity verification and tampering positioning method, and relates to the field of network security and digital document protection. The multi-feature information hidden document integrity verification and tampering positioning method comprises the following steps: S1, document multi-dimensional feature extraction and encryption: based on an original Word document, adopting an SHA-3 algorithm to calculate a full-text hash value as a content feature, and extracting document object model features such as paragraph hierarchy, table structure and the like through a DOM tree analyzer; after the two types of features are combined into a feature matrix, a dynamic secret key is generated through Logistic chaotic mapping for XOR encryption, and an encrypted document feature matrix is generated. A dynamic key is generated by combining Logistic chaotic mapping encryption through a multi-dimensional extraction technology fusing document content hash values and structural features, so that the anti-cracking capability of a feature matrix is remarkably improved, the confidentiality of document features in open network transmission is ensured, the block information retention rate is simulated and predicted by adopting Monte Carlo, and the watermark embedding weight is dynamically allocated.

The invention discloses a large model generation content traceability technology based on model copyright ID watermark embedding, which comprises the following steps that: firstly, a large model content generator compiles a dynamic watermark embedding process into an arithmetic circuit, and generates a watermark text and a proof by using a zero-knowledge proof algorithm; then the large model content generator publishes the text with the watermark and the proof together, and hides the copyright ID and the secret key; a large model content user verifies the proof and the watermarked text through a smart contract by adopting a zero-knowledge verification algorithm; after verification is passed, an adversarial sample corresponding to the text with the watermark is generated, an adversarial training method is used for optimizing the text with the watermark, and parameters of the Viterbi balance algorithm are dynamically adjusted and improved. According to the method, the concealment and the generation quality are balanced, high-capacity watermark embedding is realized, the tamper resistance and the accurate traceability of multi-model copyright disputes are improved, and the privacy protection intensity is improved.

The invention provides an image watermark generation method, an image watermark generation device and a computer storage medium. The image watermark generation method comprises the following steps: acquiring original potential features of an original image; watermarking information with the same size as the original potential features is fused with the original potential features, and fused potential features are obtained; acquiring a pixel-level visual sensitivity mask parameter of the original image; adjusting the watermark intensity of the fused potential feature by using the pixel-level visual sensitivity mask parameter to obtain an adjusted potential feature; generating an image watermark according to the adjusted potential features; and generating a watermark image according to the image watermark and the original image. According to the image watermark generation method, the watermark embedding strength is adaptively adjusted by using the pixel-level visual sensitivity mask parameters, so that global collaborative adaptation of watermark energy distribution and image visual characteristics is realized, and the robustness of the watermark to composite attacks is remarkably improved.

The invention discloses a digital collection full-life-cycle traceability tracking system based on a block chain, and the system comprises a digital identification module, a verification module, a packaging transfer module, a verification module, an integration module, and a system construction module, and achieves the full-life-cycle traceability tracking of a digital collection through the technologies of multi-dimensional feature extraction, watermark embedding, distributed storage, and zero-knowledge proof. And tracking and verification of the whole process from creation to transaction of the digital collections are realized. According to the system, the transaction authorization model is encapsulated by adopting the intelligent contract, so that safe transfer of ownership is ensured; a tamper-proof verification mechanism is constructed through multi-level integrity verification; different platform data are integrated by using a cross-chain technology to form a comprehensive traceability graph, so that key problems of authenticity verification, copyright protection, value evaluation and the like faced by a digital collection market are effectively solved. In addition, the system also establishes a scientific authenticity evaluation and value dynamic evaluation system, provides an objective basis for market pricing, and significantly improves the credibility and safety of the digital collections.

The invention discloses a multistage video watermark embedding and extracting method and system, and the method comprises the steps: carrying out the perception analysis of an original video through a space-time attention neural network, and dynamically dividing a time domain stable region and a space domain sensitive region; a three-level watermark system is constructed, the three-level watermark system comprises a basic-level watermark, a first-level enhanced watermark and a second-level enhanced watermark, the basic-level watermark adopts an anti-rotation binary matrix generated by a Fourier descriptor, the first-level enhanced watermark is a DCT frequency domain Hash sequence, and the second-level enhanced watermark is a semantic fingerprint generated based on a motion vector. According to the method, through multi-dimensional collaborative optimization of the anti-attack capability, the dynamic security mechanism and the verification efficiency, the core defects that erasing is easy due to insufficient reversibility in a traditional video watermarking technology and counterfeit affiliation chaos is caused by a static key are overcome, and reliable technical guarantee is provided for digital content copyright protection.

The invention discloses a screen shooting traceability method, device and equipment and a readable storage medium, which are applied to the field of computer software, and comprise the following steps: encoding watermark information to obtain a watermark information sequence; embedding the watermark information sequence into the image through an R channel, a G channel and a B channel of RGB (Red, Green, Blue) to obtain an image with a watermark; and acquiring the shot image with the watermark image, and tracing to obtain watermark information based on the shot image. According to the method, the watermark is generated by using the RGB channel of the single pixel image, so that the visual concealment of the watermark image is improved, the difficulty in perceiving of human eye recognition is ensured, and the visual quality of a user is not influenced; the watermark embedding position is not influenced by an original image carrier, and watermark information can be hidden in any area of the image; and the watermark information recovery success rate is high.

The invention discloses a digital archive safe circulation tracking method and system based on a dynamic watermark, and relates to the technical field of digital archives, and the method comprises the steps: collecting the physical layout features and semantic features of an archive at the same time, generating a comprehensive archive feature map, and dynamically adjusting the watermark intensity in combination with a rank screening threshold; a visual fidelity and watermark extraction degree double-index evaluation system is introduced, watermark position evaluation is carried out, and archive carrier sequence substandard and standard labels are obtained; and by monitoring the watermark survival rate, the visual fidelity and the circulation error rate, a correction strategy selection rule is constructed and comprises position iteration correction or sequence correction, so that evaluation and dynamic calibration of the watermark embedding position are realized, and dynamic selection of a correction strategy is realized.

The invention relates to the technical field of digital watermarking, and discloses a self-adaptive image watermark embedding and recovering system, which comprises an image preprocessing module used for carrying out discrete wavelet transform on a carrier image to be embedded with a watermark, decomposing the image into a plurality of frequency sub-bands, and selecting a low-frequency sub-band as a watermark embedding region; the watermark preprocessing module is used for carrying out orthogonal transformation on the watermark image, so that orthogonality is kept among a plurality of watermarks, and mutual interference of the watermarks is reduced; and the watermark embedding module is used for embedding the watermark into the low-frequency sub-band of the carrier image based on the output of the watermark preprocessing module. According to the method, the concealment and robustness of the watermarks are improved through the technologies of self-adaptive optimization of the watermark strength, redundancy embedding and orthogonal transformation, and compared with the prior art, the interference between the watermarks is effectively reduced, and the resistance of the watermarks to attacks such as compression, noise, cutting and rotation is enhanced.

The invention discloses a watermark imperceptible embedding and recovering method based on a deep learning network structure. Firstly, a mask guide watermark embedding scheme is designed, a mask generation module is constructed by using a residual dense feature extraction module and an attention mask generation module, and a watermark is adaptively guided to be embedded into an image texture rich area so as to improve the invisibility of the watermark. And secondly, constructing a watermark decoding network based on comparative learning, and by comparing a loss function, taking the decoding features of the same watermark image under different noise conditions as positive samples and taking the decoding features of different watermark images as negative samples so as to enhance the consistency of the decoding features, thereby improving the robustness of the watermark. The deep learning network structure can improve the robustness of the watermark in a real screen shooting scene, and has a huge application value in copyright protection and traceability tracking.

The invention discloses a wide-area time traceability credible authentication method in a power heterogeneous network environment, and belongs to the technical field of power system informatization and automation. Comprising the following steps: acquiring various high-precision time source information in a power heterogeneous network environment, and acquiring an initial time reference of the power system time synchronization device; generating uniform reference time, and embedding a digital watermark containing time source information and credibility thereof for each timestamp by using the digital watermark embedding module; controlling the power system time synchronization device to sequentially generate time transmission fingerprints at all levels of network nodes; constructing a unique time fingerprint chain covering the whole link; packaging the time fingerprint of each time transmission node and the traceability information thereof, and performing uplink storage; precise prediction and optimal control of time transmission errors are realized; if the overall cumulative time error exceeds a range of + / -10 [mu] s, the predictive correction is maintained until the error is controlled within + / -10 [mu] s.

The invention belongs to the field of image processing, and relates to a method for embedding a robust watermark in a diffusion model generated image, which comprises the following steps of: acquiring cue words, initial Gaussian noise and watermark information, and inputting the cue words, the initial Gaussian noise and the watermark information into a trained diffusion model based on watermark embedding to obtain a watermark-embedded image; the training process of the diffusion model comprises the following steps: acquiring cue words, initial Gaussian noise and watermark information, and inputting the cue words, the initial Gaussian noise and the watermark information into an encoder to obtain potential vectors; inputting the potential vector into a self-attention module to obtain an embedded position vector; embedding the watermark information into the potential vector according to the embedding position vector; inputting the potential vector embedded with the watermark information into a decoder to obtain an image embedded with the watermark; extracting watermark information from the image embedded with the watermark; updating parameters of a diffusion model according to the image embedded with the watermark and the extracted watermark information until a trained diffusion model is obtained; according to the method, the embedding position is selected by combining the potential of the diffusion model and the accuracy of the self-attention mechanism, so that efficient watermark embedding and extraction are realized.

The invention provides a large language model watermark embedding and detecting method and system based on entropy adaptive adjustment, and belongs to the technical field of large language models. The problems that an existing method is low in low-entropy text detection reliability and affects the text quality are solved. The method comprises the following steps: S1, calculating an entropy value of a token to be generated in real time through an entropy analysis module, and dynamically generating a watermark intensity control parameter; s2, adaptively adjusting a watermark embedding process according to the watermark intensity control parameter, and outputting a watermark text; and S3, calling a weight calculation module, and quantifying the confidence of the watermark signal in combination with the watermark intensity control parameter. The method is mainly used in the field of text source recognition.

The invention discloses a self-adaptive multi-level digital watermarking method based on coding process optimization. The method comprises the following steps: firstly, performing deep preprocessing on an input video stream through a pre-trained multi-modal deep learning model, extracting space complexity, time dynamics and content significance features, and generating a uniform feature vector; and constructing a dynamic decision model based on the feature vectors, adaptively determining the watermark intensity, the embedding position and the type, and realizing accurate matching of watermark parameters and video content characteristics. A multi-level watermark hierarchical embedding mechanism is adopted, a robust invisible watermark, a fragile invisible watermark and a dynamic visible watermark are embedded into a video, and the security is enhanced by combining chaotic encryption or Hash chain encryption. During copyright verification, watermark information of each layer is recovered through an adaptive extraction algorithm, and data verification and infringement traceability are completed in combination with a block chain evidence storage system. According to the method, a full-process copyright protection system of embedding, coding, extraction and verification is constructed.

The invention discloses a watermark generation detection method based on a stochastic smoothing testible robust large language model. The method comprises the following steps: training a watermark generator according to a lexical sequence data set; according to the prompt word text data set, the watermark generator and the large language model, generating texts with embedded watermarks and texts without embedded watermarks, and training a watermark detector; and converting a text into which a preset watermark is to be embedded, inputting the converted text into the trained watermark generator, outputting the text into which the watermark is embedded, processing the text into which the watermark is embedded by adopting a random smoothing-based mode, and inputting the processed text into the trained watermark detector for detection, thereby realizing watermark generation detection. According to the method, the watermark can still be reliably detected after the text is tampered, the anti-attack ability of the watermark is improved by introducing the random smoothing technology, the watermark detector has the cross-model generalization ability through the code conversion and training strategy, and efficient watermark embedding and detection can be achieved while the text generation quality is kept.

The invention discloses a controllable text watermark embedding method based on a reinforcement learning strategy model, and the method comprises the steps: constructing a reinforcement learning strategy model, and controlling the token output of a large language model through the reinforcement learning strategy model; under the strategy control of the reinforcement learning strategy model, watermark embedding is carried out on the output text based on a watermark embedding strategy, and a candidate token sequence with hidden information is formed. According to the method, a policy network based on a reward function is introduced to perform token-level dynamic intervention on a large language model generation process, multiple embedding modes such as vocabulary replacement, grammar disturbance and structure addition are fused, and a watermark detector is used for providing a feedback signal to realize end-to-end closed-loop optimization; therefore, the naturalness, robustness and detectability of the text watermark are remarkably improved on the premise that the main structure of the language model is not modified, and the method has good deployment flexibility and traceability.

The invention discloses a database watermark management system and method, electronic equipment and a storage medium, and relates to the technical field of information security, and the disclosed database watermark management system comprises an intelligent decision layer which is used for analyzing data features of input data and determining a watermark embedding mode of the input data according to an analysis result; the bimodal embedding layer is used for carrying out watermark embedding operation on first data of which the watermark embedding mode is a numeric embedding mode in the input data by adopting a preset improved least significant bit algorithm, and carrying out watermark embedding operation on second data of which the watermark embedding mode is a non-numeric embedding mode in the input data by adopting a preset improved least significant bit algorithm; carrying out watermark embedding on the second data by adopting a multi-system invisible character replacement technology; and the cross-modal traceability layer is used for analyzing the watermark information from the input data embedded with the watermark through a preset double-watermark collaborative verification mechanism. According to the method, the adaptation degree of database watermark embedding can be improved.

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for processing a watermark of a neural network model. The method includes: embedding a parameter component watermark into a first parameter of a neural network model to generate a second parameter of the neural network model; embedding an input component watermark into a first input to the neural network model to generate a second input to the neural network model; embedding a gradient component watermark into a first model gradient of the neural network model to generate a second model gradient of the neural network model; and training the neural network model based on the second parameter, the second input, and the second model gradient to generate a trained neural network model.

The invention discloses an electronic document tracing method and device based on dynamic encryption and multi-modal watermarking, and relates to the technical field of information security and digital rights management. The method comprises the following steps: acquiring a PDF document uploaded by a user, encrypting the PDF document by adopting a randomly generated main encryption key, and acquiring and storing the encrypted PDF document; the content of the encrypted PDF document is analyzed, secret information is embedded into a picture in the document based on a two-dimensional discrete cosine transform algorithm, and picture watermark embedding is completed; a self-adaptive watermark embedding algorithm is adopted to embed secret information into a text in the document, and text watermark embedding is completed; related information of the checking operation is written into a block chain database; dynamically extracting watermark information from the divulged PDF document by adopting a watermark extraction algorithm; and querying a traceability database according to the extracted watermark information, and outputting a traceability result by comparing the extracted watermark information with information in a database storing various watermark information. According to the invention, the survival rate and the concealment of the watermark can be improved.

The invention relates to a deep forgery active defense method based on watermarking, which belongs to the technical field of information security, firstly provides a semantic self-adaptive watermark embedding method based on Transform, and dynamically distributes watermark weights according to the semantic importance of a human face area by using a cross attention mechanism so as to enhance the defense effect on deep forgery attack; secondly, a separable decoder watermark architecture is constructed, the separable decoder watermark architecture comprises a robust decoder and a semi-robust decoder, a random image processing module (RIPM) is introduced during training, the separable decoder is trained by simulating images of conventional distortion and deep forgery attack, and stable traceability of user identity information and recognition of deep forgery behaviors are achieved; meanwhile, local watermark concentration difference caused by deep counterfeiting is analyzed, a thermodynamic diagram of a counterfeiting region is generated in combination with supervised learning, and positioning of the deep counterfeiting region is realized.

The embodiment of the invention provides a method for determining a generative model. The method includes embedding a white box watermark and a black box watermark in a generative model. Black box watermarks are first embedded into probability density functions of data abstraction in different layers of the generative model. The method also includes embedding the white box watermarks into different layers for generative model output after the embedding of the black box watermarks is completed. Model data is generated by the generative model based on predetermined trigger data. The predetermined trigger data comprises a predetermined trigger text or a predetermined trigger image. An identity associated with the generative model is determined based on the model data. By using the method, white box and black box attacks can be resisted by embedding two complementary and independent watermarks, so that double-layer protection is provided for the generative model.

The invention discloses a method, a system and equipment for protecting a recoverable image based on a diffusion model, and a medium. The method comprises the following steps: acquiring a to-be-protected image and preset watermark information; an adversarial sample generation module is constructed based on projection gradient descent, an adversarial sample purification module comprising a watermark embedding sub-module is constructed based on a diffusion model, and the watermark embedding sub-module is constructed based on a decoder of a potential diffusion model; inputting the to-be-protected image into an adversarial sample generation module to generate an adversarial sample image; the adversarial sample image is input into an adversarial sample purification module for denoising, a restored image is obtained, and the restored image is a restored to-be-protected image; and adding preset watermark information into the restored image through the watermark embedding sub-module to obtain the restored image containing the watermark. According to the method, the image can be recovered and the image privacy can be better protected under the condition that the image quality is ensured.

According to the invisible watermark embedding method based on the wavelet domain statistical characteristics, the high-frequency sub-band in the wavelet domain is selected as the embedding area, and the robustness of the watermark to conventional attacks such as compression and noise is improved while invisibility is guaranteed. Based on a self-adaptive embedding weight mechanism of a covariance matrix eigenvalue, the embedding strength of different texture regions can be dynamically adjusted, the robustness is enhanced by a smooth region, and distortion is suppressed by an edge region. Nonlinear disturbance is generated by adopting a deep neural network and chaotic encryption preprocessing is combined, so that the predictability of a linear statistical rule is broken, and the method has a relatively good application prospect.

The invention relates to the technical field of generative AI watermarks, and discloses a domestic credible watermark embedding and tracing method and system based on generative AI output. An acquisition module of the system acquires original content data output by a generative AI, and establishes a trusted watermark configuration database mapped with the original content data; the watermark target setting module configures a watermark embedding target based on the database and covers watermark robustness optimization, watermark invisibility optimization and watermark traceability association optimization; the watermark classification identification module determines target classification identifications, wherein the target classification identifications comprise a main watermark target, an auxiliary watermark target and a reference holding target; after the key watermark parameters are selected by the watermark influence analysis module, the influence analysis of the key parameters on the watermark embedding target is executed; the watermark traceability module establishes a watermark optimization constraint according to the influence analysis result, executes watermark parameter directional embedding, and completes localized credible watermark embedding; and the traceability path construction module generates a traceability path based on the embedding result to obtain a traceability verification result.

The invention relates to the technical field of artificial intelligence and audio processing, can be applied to the field of intelligent medical treatment and financial science and technology, and discloses an audio watermark processing method and device, equipment and a medium, and the method comprises the steps: carrying out the coding of continuously inputted audio blocks, and obtaining an audio feature vector; acquiring a digital watermark, and performing time modulation on the digital watermark according to the audio lengths of the blocks by using a multi-layer perceptron to obtain a watermark implicit vector; embedding the watermark implicit vector into the audio feature vector to obtain a target audio vector; performing quantization processing on the target audio vector to generate a target audio spectrum embedded with a digital watermark; and decoding the target audio frequency spectrum to obtain a target audio. According to the method, the continuously input audio blocks are coded, the method can adapt to a streaming scene, the audio data are processed segment by segment, the watermark embedding requirement of the continuous audio stream is met, the audio can be compressed, the data volume is effectively reduced, the transmission time consumption is reduced, and the low-delay communication requirement is met.

The invention provides a teaching document anti-copy protection method and system based on OCR interference resistance, and belongs to the technical field of information security and digital copyright protection. According to the method, anti-OCR font processing is applied to document characters, invisible disturbance symbols are inserted between characters or paragraphs, watermark information is embedded in document typesetting or font strokes, and finally a standard PDF or DOCX file is compiled. Watermark information is embedded through stroke thickness change, paragraph row spacing indentation perturbation and other modes. The system comprises a font disturbance module, a character disturbance module, a watermark embedding module, a compiling output module and a verification module. According to the method, the human eyes can normally read the document, but messy codes or disorder can occur when the OCR tool extracts the document, and the document cannot be directly reused; meanwhile, watermark information cannot be stripped, so that leakage tracking is facilitated; and the output file keeps a standard format and is compatible with the existing office teaching process. The method is suitable for anti-copying protection of teaching documents, scientific research data, test papers, contract files and the like.

The invention discloses a watermark embedding and detecting method and system based on a large model. The method comprises the following steps: acquiring a real text sequence of a watermark to be embedded; constructing a watermark embedding and detecting model based on a large model; and completing watermark embedding and detection of a real text sequence by using the watermark embedding and detection model. According to the method, the watermark is directly embedded in the model training process, and the detection mechanism is optimized, so that efficient embedding and accurate detection of the watermark are realized. A generative adversarial network and a binocular telescope structure are innovatively introduced, so that the natural fluency of a generated text is ensured, and the robustness of a watermark under adversarial attack and natural rewriting is remarkably enhanced. Meanwhile, the calculation cost is reduced by adopting a low-rank adapter technology, so that the method can be widely applied to various pre-training large models and has very high adaptability.

The invention relates to an image watermark generation method and device based on a thinking chain strategy and computer equipment. The method is based on a visual perception visual large model, after knowledge enhancement is carried out on the visual large model through expert knowledge, the visual large model firstly analyzes the content of a picture, then a watermark scheme is given, then a segmentation model and an edge optimization module are called, and finally a watermark is rendered to the image. According to the method, the balance between the visual effect of the artwork and copyright protection is fully considered in the watermark generation process, and the technical path from art aesthetics, image processing to watermark embedding ensures that the generated watermark not only has high aesthetics, but also can effectively ensure the visibility and non-tampering property of the watermark.

The invention discloses a watermark embedding method, a watermark extraction method, equipment, a readable storage medium and a product, and relates to the technical field of image processing, and the method comprises the steps: obtaining an original image to be subjected to watermark embedding and watermark information to be embedded, and inputting the original image and the watermark information to a pre-trained watermark embedding model; performing feature extraction on the original image through a feature extraction network to obtain a multi-domain fusion feature; a watermark embedding mode is determined through an adaptive attention network according to the multi-domain fusion features, and the watermark embedding mode at least comprises a watermark embedding position; watermark information is embedded in the multi-domain fusion features in a residual error mode through a watermark embedding network based on a watermark embedding mode, and embedded features are obtained; and through an image reconstruction network, the embedded features are converted to an airspace to obtain a watermark image after watermark embedding. According to the method, the watermark robustness is ensured, and meanwhile, the influence on the visual quality of the image is reduced.

The present disclosure relates to digital watermarking systems that may use visual masks to optimize watermark embedding in digital imagery. A visual mask provides guidance for adjusting digital watermark signal strength, enabling improved trade-offs between watermark imperceptibility and robustness. Multiple embodiments generate visual masks including: (1) a Perceptual Modeling Candidate approach using contrast masking and texture classification based on standard deviation mapping; (2) a wavelet-based approach using Dual-Tree Complex Wavelet Transform for translation-invariant frequency analysis; (3) artificial intelligence approaches employing convolutional neural networks trained to optimize embedding strength while minimizing perceptual distance metrics such as LPIPS; and (4) LPIPS threshold masking that determines optimal embedding strengths by testing multiple candidate strengths. Visual masks enable content-adaptive digital watermarking that places stronger signals in textured regions while maintaining imperceptibility in flat regions, improving visibility-robustness performance compared to uniform embedding approaches.

The invention discloses a generative model redrawing attack-oriented image robust watermark traceability method and system and a computer readable storage medium, and belongs to the field of digital information security and artificial intelligence content governance. The method comprises the following steps: in a watermark embedding stage, performing error correction coding and digital signature processing on traceability information containing identity information and a timestamp to generate a load to be embedded, and embedding the load to be embedded and a synchronization template for geometric synchronization into a host image in a function separation manner by using a deep neural network; in the training stage, combined optimization is carried out on the watermark embedding and extracting process by introducing generative model redrawing and microsimulation of image distortion attack, so that the robustness of the watermark under a complex attack condition is improved; in the extraction and verification stage, under the condition that an original image is not needed, geometric synchronous correction and blind extraction of a watermark load are carried out on an image to be analyzed, and authenticity confirmation of traceability information is completed through error correction decoding and digital signature verification. By adopting the technical scheme of the invention, the problems of insufficient traceability information robustness, difficult source confirmation and incomplete evidence chain in a generative model redrawing scene in the prior art are solved, and verifiable traceability and credible evidence generation of the image source information are realized.