432 results about "Watermark embedding" patented technology

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 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.

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.

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 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.

The invention discloses a watermark processing method and device, a storage medium and a computer readable storage medium, and relates to the technical field of digital watermarking. The watermark processing method comprises the following steps: capturing a current data packet flowing into a target gateway; according to the grading label of the current data packet, the corresponding watermark embedding strength is determined, and the watermark embedding strength is in positive correlation with the priority degree corresponding to the grading label; obtaining identification information of the current data packet, and generating a dynamic digital fingerprint based on the identification information; and embedding the dynamic digital fingerprint into the current data packet based on the watermark embedding strength to obtain a target data packet, and sending the target data packet to a specified delivery address corresponding to the current data packet through the target gateway. The technical problem that an existing watermark processing method is difficult to effectively protect real-time data streams is solved.

The invention discloses a copyright protection method for a 3DGS three-dimensional scene and objects in the scene, and belongs to the technical field of three-dimensional reconstruction. Target objects needing to be protected are automatically extracted from a multi-view image data set through an image sequence segmentation model; combining the original image with the target object image to construct a new data set for watermark network training, and carrying out network training; performing high-quality scene reconstruction on the multi-view image by adopting a 3DGS method; a hidden information loss function is introduced to finely adjust scene color information and a decoder of a watermark network, and copyright watermark embedding of the whole scene and a target object is achieved; the frequency domain consistency loss and the image difference loss are combined and optimized, and the quality of the reconstruction model after the watermark is embedded is ensured. According to the method, copyright protection of a three-dimensional scene level can be realized, fine-grained protection can be carried out on specific objects in the scene, and the method has relatively high practicability and robustness and is suitable for application scenes such as digital asset management and virtual reality content protection.

The invention discloses a DCT (Discrete Cosine Transform) frequency domain watermark embedding optimization method and system based on a potential space, and the method comprises the steps: firstly coding an original image into the potential space, segmenting the original image into small blocks, embedding a single-bit watermark into the frequency domain of each small block through weighted voting, then carrying out the inverse transformation to a spatial domain, merging, and forming a potential representation with a watermark; decoding through a stable diffusion model to generate an image with a watermark; iteratively optimizing the potential representation through a multi-objective loss function until the loss is minimum; and finally, decoding the optimized representation again, linearly mixing with the original image, adjusting a mixing coefficient through binary search, and outputting a final image on the premise of meeting preset image quality. According to the method, a multi-dimensional robustness optimization strategy is combined, the stability and the non-erasable property of the watermark under the attack of the generative model are systematically improved, and the core requirements of AI copyright protection and content traceability are met.

The invention discloses an audio watermark embedding method, an audio watermark extracting method and a model training method, and belongs to the technical field of artificial intelligence. The audio watermark embedding method comprises the following steps: acquiring watermark data and an original audio clip in which a watermark needs to be embedded in original audio data; inputting the original audio clip and the watermark data into an encoder, and determining embedding coefficients of at least two time frames in the original audio clip through the encoder; based on the embedding coefficient, the watermark data is embedded into the original audio clip, a watermark-containing audio clip corresponding to the original audio clip is obtained, and the embedding coefficient of the time frame is in positive correlation with the energy of the time frame; and replacing the original audio clips in the original audio data with the audio clips containing the watermarks to obtain the audio data containing the watermarks.

The invention provides a watermark embedding method, computer equipment and a computer readable storage medium, and the method comprises the steps: positioning a watermark position in a target text based on a preset watermark position positioning rule; based on the content type at the watermark position, determining a watermark setting type to which the watermark position belongs, adopting a watermark setting strategy corresponding to the watermark setting type to determine watermark information of the watermark position and an arrangement mode of the watermark information; and generating a cue word by using the target text, the watermark position, the watermark information and the arrangement mode of the watermark position and a preset watermark, taking the cue word as input information of the large oracle model, and outputting a watermark text corresponding to the target text through the large language model. According to the technical scheme, the hidden marking and copyright protection of the text content are realized through the deep cooperation of the multi-dimensional watermark setting strategy and the large language model.

The invention discloses a multi-party supply chain sensing data encryption measurement and traceability method based on space-time coupling watermark embedding, which comprises the following steps of: constructing a double-domain coupling model of a time sequence and a space distribution characteristic, and embedding dynamic watermark information into a sensing data stream to realize data source identity identification and tamper-proof protection. A self-adaptive key generation and credible chain verification mechanism is introduced in an encryption metering stage, so that the data integrity and confidentiality can be guaranteed on the premise of not influencing the real-time transmission performance of the data; in the tracing stage, a data transmission path is reconstructed by using the watermark tracing matrix, and data responsibility definition and anomaly detection among multiple nodes are realized. The method is suitable for high-security data management and credible tracking in a supply chain multi-node collaborative environment.

The embodiment of the invention provides a watermark embedding method and device suitable for a diffusion model, equipment and a storage medium. The method is applied to the technical field of image generation, a watermark set is constructed, watermark information is randomly extracted from the watermark set and input into a pre-trained diffusion model, and each piece of watermark information in the watermark set is in one-to-one correspondence with a user identifier; the parameters of the middle layer of the pre-trained diffusion model are adjusted through a preset fine adjustment module, a fine-adjusted diffusion model is obtained, and the fine adjustment module comprises a convolution layer, a self-adaptive average pooling layer, a first full-connection layer, a second full-connection layer and a third full-connection layer; and embedding the input watermark information into a generated image of the diffusion model by using the fine-tuned diffusion model to obtain a target output image, the target output image including the watermark information. According to the method, the watermark information is embedded in the process of generating the image by the diffusion model, so that the calculation cost is reduced, and the watermark embedding stability and reliability are improved.

The invention discloses a black-box watermark embedding method for a neural network text classification model, and the method comprises the steps: firstly constructing a watermark trigger set containing a semantic-level trigger and a character-level trigger at the same time, and then randomly mixing the constructed watermark trigger set with a common training sample to form a mixed data set; the classification model is trained based on the mixed data set, so that watermark information is encoded in the trained classification model, and the optimization target of training is to minimize an overall loss function; the method can effectively solve the problem that a trigger set sample destroys semantic discreteness and structure of a text, and meanwhile, due to the design of the double triggers, the intensity of a watermark signal is improved, it is ensured that when the trigger set is subjected to preprocessing operation such as text cleaning and the like and one type of triggers are missing, the text can still retain the other type of triggers, and the use experience of the user is improved. And through embedding at different levels, the robustness of the watermark for resisting attacks is obviously enhanced.

Methods, devices, and processor-readable media for digital image watermarking, including performing one or more decoding operations that each include generating a respective intermediate watermark embedding feature tensor that includes one or more representations of a watermark message embedded into a feature tensor generated by a preceding decoding operation; upscaling the respective intermediate watermark embedding feature tensor to generate a further feature tensor; and generate a watermarked image based on the further feature tensor.

The invention discloses a spatial self-adaptive plug-and-play watermarking method and device for image editing traceability, and the method comprises the steps: obtaining the hidden space feature representation of an edited image through an image editing model according to an input original image and an editing condition; constructing a structured watermark grayscale image used for bearing image editing traceability core information, and encoding the structured watermark grayscale image into watermark potential features through a watermark encoder; based on the watermark potential features, watermark embedding of content and structure perception is realized in the submerged space, and the submerged space feature representation of the edited image with the watermark is acquired; and inputting the hidden space feature representation of the edited image into an image decoder, decoding the edited image into an edited image with a watermark, inputting the edited image with the watermark into a watermark decoder, extracting an embedded watermark grayscale image, and restoring complete traceability metadata information based on the watermark grayscale image. The device comprises a processor and a memory. According to the invention, high visual quality of the image is maintained while reliable traceability is realized.

The invention relates to the technical field of large language model security, and particularly provides a large language model watermarking method based on a triple word list division strategy. The method comprises a watermark embedding step and a watermark detection step, wherein in the watermark embedding step, a vocabulary is dynamically divided into three mutually exclusive subsets of green, yellow and red based on context information at each time step; dynamically determining a gating value according to the context entropy value; applying a positive bias to the green subset vocabulary and a negative bias to the yellow subset vocabulary based on a gating value, and prohibiting selection of the red subset vocabulary, thereby adjusting an output probability distribution and embedding a watermark; in the watermark detection step, a subset corresponding to each word is reproduced, the hit rate of green and red in a sliding window is calculated, statistical significance is calculated based on Poisson-binomial distribution, and the existence of the watermark is judged through Fisher merge test. According to the method, the problems of low detection success rate, high false alarm rate, text quality reduction and insufficient robustness of the existing watermark technology are solved.

The invention provides an image watermark embedding method and device, electronic equipment and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: acquiring an original image, text semantic information of the original image and a watermark of the original image; and inputting the original image, the text semantic information and the watermark into the image watermark embedding model to obtain a watermark image output by the image watermark embedding model. According to the method, the local features and the second global features of text enhancement are fused, and a cross attention mechanism is combined, so that the problems of low watermark invisibility and poor robustness caused by neglecting global semantic information in the existing image watermark embedding are solved, and the coordination of the embedded watermark and the original image is improved.

The invention discloses an Internet of Things video monitoring big data privacy protection and efficient retrieval method based on artificial intelligence, belongs to the crossing field of artificial intelligence, Internet of Things and information security, and is suitable for vehicle-mounted, park, battery swap stations and other scenes. The method comprises the following steps: firstly, establishing a self-adaptive acquisition framework, and realizing multi-protocol switching and video preprocessing; secondly, extracting privacy information through an improved YOLO algorithm and a Graph-Cut technology, and combining reversible watermark embedding; constructing a multi-dimensional privacy level model for hierarchical encryption, and matching two-level storage and three-level index; then, two-factor authentication authorization is performed to extract privacy and optimize retrieval; and finally, the system state is monitored in real time and self-adaptive optimization is performed. According to the method, the problems of protocol heterogeneity, insufficient privacy protection, low retrieval efficiency and the like can be solved, and privacy security, storage overhead and retrieval efficiency are balanced.