237 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 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 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 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 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 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 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.

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 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.

The invention discloses a screen watermark embedding method and device based on deep learning, and relates to the technical field of image watermark embedding. Obtaining a to-be-displayed original image and an original watermark image; inputting the original image into a preset adaptive intensity prediction network to obtain a current embedded intensity graph; according to the current embedding strength graph and the original watermark graph, additive watermark embedding is carried out on the original image, and an image with a watermark is obtained; calculating the image with the watermark and the original image to obtain an image quality evaluation value; if the image quality evaluation value meets a preset quality threshold value, outputting the currently generated image with the watermark as a final embedding result image; and if the image quality evaluation value does not meet the preset quality threshold value, multiplying the current embedding strength graph by an attenuation coefficient, and taking the attenuated embedding strength graph as a new embedding strength graph. By implementing the provided technical scheme, the problem that the image quality is difficult to accurately control by a fixed model is solved.

The invention discloses a watermark embedding and watermark tracing method and device for digital information, equipment and a medium, and relates to the technical field of digital watermarking, and the method comprises the steps: generating an initial watermark based on the Redis atom incremental operation when carrying out the watermark embedding of a plurality of initial sensitive digital information, and segmenting the initial watermark to obtain a plurality of initial sub-watermarks; randomly selecting an initial sub-watermark as a target watermark, and respectively embedding the target watermark into each piece of initial sensitive digital information to obtain each piece of target sensitive digital information containing the watermark; and when watermark tracing is carried out on a plurality of pieces of target sensitive digital information, the to-be-confirmed watermarks corresponding to the target sensitive digital information are analyzed based on a watermark extraction algorithm, the target watermarks are determined from the to-be-confirmed watermarks, and corresponding watermark tracing results are generated. According to the method, protection of sensitive digital information is realized through a watermark embedding and tracing scheme which is high in concealment and robustness and is adaptive to multiple types of short data.

The application relates to the field of generative artificial intelligence, and discloses an ethical control and content authentication method of generative AI, which comprises the following steps: analyzing generative content characteristics and predicting attack modes, dynamically adjusting a watermark embedding strategy, and enhancing robustness by using redundant coding, optimizing watermark embedding by using game theory, guaranteeing watermark integrity, verifying watermark effectiveness by using multi-modal authentication analysis, and finally realizing automatic copyright protection by using blockchain and smart contract; the application also provides an ethical control and content authentication system of generative AI, which comprises a content analysis module, a watermark embedding module and the like. Through a game model between a protector and an attacker, optimal dynamic adjustment of the watermark embedding strategy is realized, the watermark can be adaptively adjusted to ensure its effectiveness under different attack modes, and the problem that the watermark is easily tampered with or removed under a confrontational attack is effectively avoided.

The present application provides a watermark embedding method and a display device. The watermark embedding method comprises the following steps. An input image is received by a processing circuit. A watermark signal is generated by the processing circuit according to time series data and a preset scheme to generate watermark information of the watermark signal. In each of a plurality of consecutive periods, the phases of the time series data are opposite and the average values of the gray scales of the preset scheme are equal during a dark state period and a bright state period. The watermark signal is embedded into the input image signal by the processing circuit to generate an output image signal with the watermark information. An image is displayed by a display panel according to the output image signal.

This watermark embedding device includes: a watermark embedding unit that receives an electronic watermark and outputs embedding information for specifying a signature generation circuit in which the electronic watermark is embedded; and a signature generation unit that receives messages and outputs electronic signatures obtained by applying the signature generation circuit to the messages. However, the probability of how well the content of the electronic watermark can be identified by an interactive device on the basis of an operation is equal to or less than a reference, said interactive device performing said operation involving issuing any of the messages to a trial device to which the signature generation circuit can be applied and receiving the electronic signature obtained by applying the signature generation circuit to the message with the trial device.

The disclosure provides a feature point detection-based anti-screen camera robust watermark embedding, extraction method and system, relates to the technical field of digital media copyright protection, and when a carrier image is photographed and watermark is extracted, an image correction algorithm based on PCA and SURF is proposed, the generated distortion can be well corrected, and the robustness of the screen camera watermark is indirectly ensured. The image after the screen camera can be corrected to restore the image to the state in the screen, and the selection of the feature area before and after the correction is also performed to improve the embedding effect of the watermark. In order to improve the robustness of the screen camera watermark, in the embedding of the screen camera watermark, the carrier image is subjected to DCT transformation, and then experiments are designed to select the DCT coefficients of the embedded watermark. Thus, after the watermark is embedded, the robustness is high, and the invisibility is also high.

The invention discloses a blind watermark embedding and extracting method and device based on feature enhancement and a medium. The method comprises the following steps: performing normalization processing on a color host image and a watermark image; performing feature extraction on the processed host image by adopting a wavelet feature enhancement encoder to obtain a deep enhancement feature; performing vector flattening operation on the processed watermark image to obtain a watermark vector; according to the deep enhancement feature and the watermark vector, an exponential function is adopted to improve an embedding rule to carry out watermark embedding operation, and a watermark embedding singular value matrix is obtained; performing feature reconstruction on the watermark embedded singular value matrix by adopting a wavelet feature enhanced decoder; and carrying out convolutional mapping on the reconstructed features to obtain a host image containing the watermark. By designing a wavelet feature enhancement encoder and a wavelet feature enhancement decoder, lossless decomposition, differential enhancement and precise fusion of high-frequency and low-frequency features of an image are realized, and feature extraction precision is improved, so that watermark embedding robustness and a host image visual effect are optimized.

The invention discloses a video watermark embedding and extracting method and system, and the method comprises the steps: carrying out the preprocessing of an original video frame sequence, and obtaining a preprocessed watermark video frame; and inputting the watermark video frame into a decoder based on a 3D convolutional neural network to output the extracted watermark information, so that the problem of watermark flicker can be effectively solved.

The invention discloses a diffusion model watermark embedding method and device, and relates to the technical field of artificial intelligence. The computing device obtains a first diffusion model, trains the first diffusion model according to an encoder in the teacher model to obtain an intermediate diffusion model, and trains the intermediate diffusion model according to a decoder in the teacher model to obtain a second diffusion model; wherein the intermediate diffusion model can generate a three-dimensional model with a first watermark, the second diffusion model can generate a three-dimensional model with a second watermark, and the matching degree of the second watermark and the target watermark is greater than the matching degree of the first watermark and the target watermark. The first diffusion model is adjusted in a segmented manner through the encoder and the decoder, so that the first diffusion model learns features of the stage of generating the three-dimensional model with the target watermark, and the obtained second diffusion model not only can reserve model parameters for generating the three-dimensional model as much as possible, but also can update model parameters for generating the watermark. Therefore, the availability of the second diffusion model is improved.

The invention relates to a robust image watermark embedding method based on fixed neural network steganography, and belongs to the technical field of computers. According to the method, the problems that a traditional image watermarking technology is prone to being damaged by attacks such as compression and cutting and the fine tuning cost is high are solved, robustness is obtained by adopting a HiDDeN framework to pre-train a watermark extractor, a potential diffusion model is constructed, the decoder weight is frozen, binary watermark information is mapped into potential spatial disturbance vectors, and therefore the robustness of the image watermarking technology is improved. A learnable module is utilized to generate disturbance through an L-BFGS optimizer, a visual undifferentiated image is reconstructed through a fixed decoder, and a fixed extractor verifies the watermark. According to the method, steganography is pre-set in a generation stage, post-processing performance loss is avoided, watermark consistency, attack resistance and safety are remarkably improved in combination with a fixed network structure, and the method is suitable for digital copyright protection and content traceability.

This application provides a method for adding watermarks to an image. Belonging to the field of information security, this method involves dividing an image into blocks to obtain a target number of non-overlapping blocks. The texture complexity is determined based on the number of pixels at the block boundaries and the size of the blocks. The watermark embedding amount is determined based on the texture complexity of each block, the average watermark embedding capacity, and the initial texture complexity. The average watermark embedding capacity is determined based on the total watermark information capacity and the target number. Singular value decomposition is performed on each block to obtain the maximum singular value. The singular value after watermark embedding is determined based on the maximum singular value of each block and the watermark information corresponding to the watermark embedding amount. Inverse singular value decomposition is performed on the singular values ​​after watermark embedding in each block, and each block after watermark embedding is reconstructed to obtain a watermarked image. This method can improve the amount of watermark information and the quality of the watermarked image.

The present invention relates to a method and apparatus for hiding information in digital media using watermark embedding, and more particularly, to a method and apparatus for embedding a watermark that is robust against redundant insertion. According to one embodiment of the present invention, a watermark embedding method for digital media comprises: receiving first digital media into which a first watermark has been embedded; generating a second watermark; embedding the second watermark into the first digital media to generate second digital media; and outputting the second digital media. The resulting second digital media may be characterized in that the first watermark and the second watermark are each individually detectable.

A watermark-based audio processing method for use in the audio player including a receiver, an analog-to-digital converter, a processor, a digital-to-analog converter and a transmitter includes: the receiver receiving an audio input signal, the analog-to-digital converter converting the audio input signal into a digital input signal including a keyword, the processor detecting an energy of the digital input signal, if the energy exceeds a threshold, the processor determining whether a watermark is included in the digital input signal, if not, the processor embedding the watermark into a predefined digital audio signal to generate a digital output signal, then the digital-to-analog converter converting the digital output signal into an audio output signal, and finally, the transmitter outputting the audio output signal for playback.

The application relates to the technical field of user identity privacy protection, and discloses a data illegal propagation accountability method and device based on a time-varying identity watermark. The method generates a time-varying identity TDID and a dynamic verifiable expression DVP of a user based on a timestamp, prevents malicious users from performing privacy information association analysis on the user by collecting a large amount of distributed identity verifiable expression information VP, embeds the signature of the TDID and the request access timestamp as a watermark into an owner data file, and stores the TDID and a DID identifier in a chain. When the authorized shared data is secondarily non-authorized to be propagated or accessed, the file watermark is extracted, an accountability smart contract is called, the non-authorized data propagator can be accurately located, and fast accountability is realized.

This application discloses a method, apparatus, and storage medium for disaster recovery data synchronization verification based on quantum key distribution. The method includes: establishing an initial quantum session key shared by the source and disaster recovery ends through a quantum key distribution channel, and generating a byte offset table; processing the data to be synchronized using a content-driven variable-length block and quantum random mask algorithm to obtain a quantum fingerprint data block and a data digest; performing quantum signature on the data digest using the quantum session key, and embedding the signature value into the data block according to the byte offset table to form a watermarked data block; generating a semantic fingerprint and a two-factor binding credential; constructing a synchronization data packet containing the watermarked data block, semantic fingerprint, binding credential, and historical chain node values, and sending it to the disaster recovery end for verification; and updating the disaster recovery end data, synchronization log chain, and quantum key chain after successful verification. This application ensures the immutability and traceability of the data synchronization process through watermark embedding, semantic fingerprinting, and a two-chain structure, achieving secure transmission of disaster recovery data.

This invention belongs to the field of artificial intelligence technology and discloses a method and device for constructing an artificial intelligence training dataset and detecting copyright infringement. First, an original image dataset is acquired, and a portion of the original images are selected as images to be processed according to a preset watermark embedding rate. Then, motion parallax watermark embedding processing is performed on each image to be processed. First, near and far masks are generated based on the distance from the pixel position to the image center. Then, near and far pixel offsets are applied respectively to obtain near and far offset images, which are fused to obtain a disguised image. This disguised image is then weighted and fused with the image to be processed to obtain a motion parallax watermark image, and its classification label is modified to a preset target label. Finally, it is combined with the unprocessed original images to form a training dataset. The purpose of this invention is to solve the problems in existing data watermarking methods, such as the ease with which training data preprocessing can destroy the watermark, inconsistencies between traditional watermark generation and the characteristics of the original image, leading to watermark failure, inaccurate infringement detection, and easy removal of the watermark, thereby achieving more effective copyright protection and infringement detection.