137 results about "Watermark method" patented technology

The invention relates to the field of watermarking algorithms, and provides a watermarking method and system for generating a text by a large language model. The method comprises the steps that a prompt text and a generated text sequence are input into a generation model, and an original logic score of a next mark position is generated through probability distribution calculation; performing semantic feature extraction on the generated text sequence by using an embedding model to obtain semantic embedding representation; converting the semantic embedding representation into a watermark logic score based on a pre-trained watermark model; and performing weighted combination on the original logic score and the watermark logic score to generate a final logic score, and outputting a watermark mark based on the final logic score. According to the method, the high robustness of the watermark is improved, and the safety of the watermark is also improved.

The invention discloses an image watermarking method based on a space channel interactive attention mechanism. The image watermarking method mainly comprises the following steps: acquiring an original image and constructing an image watermarking network based on the space channel attention mechanism; inputting the original image and the watermark into a watermark-containing image generation framework to obtain a watermark-containing image; inputting the image with the watermark into an image processing noise layer to obtain a noise image; inputting the noise image into a watermark extraction framework to obtain an extracted watermark; and constraining the training of the whole image watermark network based on the space channel attention mechanism by using the total loss. The image watermark network based on the space channel attention mechanism is used for copyright protection, and high-capacity embedding of watermarks, high-quality generation of images with watermarks and high-robustness extraction of the watermarks are achieved. According to the method, a depth feature map of the watermark is generated through watermark dimension extension, remodeling and diffusion by using a multi-layer sensor and the like. Meanwhile, the feature retention loss can promote the optimization of the total loss on the network training.

The invention discloses a robust reversible watermarking method based on BFMs and adaptive normalization. The method comprises the steps of watermark embedding and watermark extraction. The watermark embedding specifically comprises the steps of generating watermark sequence information, performing integer wavelet transform on a carrier image, extracting a low-frequency region, and calculating BFMs of the low-frequency region; a proper watermark pair is selected through a private key, self-adaptive normalization is carried out according to the watermark pair, and the watermark is embedded in the normalized amplitude; reconstructing an image based on the amplitude of the embedded watermark; constructing a final reversible watermark image based on the reconstructed image; watermark extraction: judging whether the image is attacked or not; for an unattacked image, carrying out watermark extraction based on the auxiliary information; for an attacked image, watermark extraction is carried out by calculating the BFMs of the attacked image. The method has noise resistance and also has relatively good image reconstruction capability.

The invention discloses a vision enhancement robust digital dark watermarking method based on deep learning, a storage medium and equipment, and belongs to the technical field of digital image processing and information security. According to the method, a visual enhancement module comprising a self-adaptive watermark region adjustment module and a frequency enhancement module is constructed, and a multi-region local loss training mechanism and a screen shooting simulation module are combined, so that the visual quality of a watermark image is improved, and meanwhile, the robustness of the watermark image for resisting physical attacks such as screen shooting is enhanced. The specific processing flow comprises the following steps: acquiring an original image and watermark information; embedding the watermark information into the image by using an encoder, wherein an embedded region is optimized by a self-adaptive region guide matrix generated based on the image edge and gray information; in the model training process, a noise layer simulating screen shooting physical distortion is introduced, and the area weight is dynamically adjusted according to residual error distribution in the later stage of training so as to focus and optimize the image quality; watermark information is extracted from an image which may suffer from an attack by using a decoder.

The invention provides a high-fidelity anti-compression image watermarking method and system based on spectrum-airspace decoupling, and belongs to the field of information security. Firstly, the watermark information is mapped and remodeled; a watermark encoder based on multi-granularity spectrum-spatial domain feature decoupling is constructed, discrete cosine transform is introduced to filter out high-frequency components, Haar wavelet transform is adopted to realize lossless downsampling, a fast Fourier transform dynamic filter is combined to capture global semantic features, and local texture details are combined through multi-scale spatial domain volume accumulation; designing a physical perception and visual self-adaptive dual embedding strategy, and anchoring watermark energy to an anti-compression brightness channel; constructing an anti-attack layer containing differentiable JPEG compression simulation and mixed noise simulation, and participating in network training; and constructing a decoder and designing a loss function to carry out network optimization. According to the method, the robustness of the watermark under strong compression and complex black box attacks is improved, and extremely high visual imperceptibility is realized through physical and visual constraints.

The invention discloses a high-fidelity robust watermarking method based on deep reinforcement learning. The method is composed of a reinforcement learning module, a watermark encoding and decoding module and a watermark synchronization module in sequence. The reinforcement learning module automatically selects an optimal watermark embedding position in a local area of the image through an adaptive decision-making mechanism of a deep Q network to realize dynamic optimization of an embedding strategy; the watermark encoding and decoding module adopts a convolutional block attention mechanism (CBAM) and a multi-scale feature fusion structure, and realizes high-fidelity embedding and accurate extraction of watermark information while maintaining the visual quality of the image; the watermark synchronization module is based on a U2Net model structure, and completes spatial alignment of a watermark area through mask prediction and affine correction, thereby effectively solving positioning deviation caused by geometric attacks such as rotation, zooming, cutting and the like. The three components work cooperatively, and high watermark extraction precision can be kept under various compression, noise and geometric disturbance conditions. The method has the advantages of imperceptible embedding, high extraction stability, independent training and combined deployment of modules and the like, and can be widely applied to the fields of digital image copyright protection, AIGC (Artificial Intelligence Generation Content) traceability, multimedia authenticity verification and the like.

The invention discloses a text watermarking method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence and digital watermarking, and the method comprises the steps: obtaining an original text and a watermark, inputting the original text and the watermark into an embedded watermark model, and carrying out the watermark embedding operation to obtain a watermark text. The watermark embedding model comprises a gating modification mechanism, the gating modification mechanism is used for replacing a segmented word with a threshold value greater than a preset threshold value in the original text with a candidate segmented word with the same meaning as the segmented word, the watermark is embedded in the candidate segmented word, and the threshold value is used for representing the feasibility of embedding the watermark at the position of the segmented word. According to the technical scheme, through the gating modification mechanism and the candidate word segmentation generation strategy, the text modification degree is strictly controlled, the semantic coherence of the text embedded with the watermark is improved, and the watermark robustness is improved.

The invention relates to a robust watermarking method of a diffusion model based on an error correction code, which belongs to the technical field of large model watermarking, and comprises the following steps of: encoding a watermark message by using a Reed-Solomon error correction code to generate a code word containing redundancy check information; carrying out randomization processing on the code word to generate a pseudo-random bit stream; embedding the pseudo-random bit stream into a variable of an initial Gaussian potential space of a diffusion model by adopting a distribution preserving sampling technology; and finally, generating an image with a watermark through a denoising process of the diffusion model. When the watermark is extracted, reverse diffusion processing is carried out on an image which is possibly damaged to recover an approximate value of a potential space variable, a code word which is possibly damaged is extracted from the approximate value, and original watermark information is recovered by utilizing the error correction capability of a Reed-Solomon decoder. On the premise of keeping the generation performance, the robustness of the watermark to compression, noise, cutting and other potential tampering attacks is enhanced.

The invention discloses a generative adversarial network (GAN)-based recoverable watermarking resisting method, which not only can ensure that an image is protected by privacy and copyright, but also can only allow an authorized party to safely use. Specifically, an encoder-decoder-restorer architecture based on a GAN (Generic Area Network) is innovatively designed, and watermarking-resistant embedding and extraction and image restoration are established as a unified task. And a dual optimization strategy is designed to resist loss and a dynamic joint training strategy, so as to achieve ideal balance among image quality, copyright protection, privacy protection and image recovery capability. Experimental results show that the method provided by the invention can ensure that the authorized DNN classifier recovers the protected image to perform accurate classification and identification when needed, and meanwhile, excellent privacy and copyright protection capability is also provided.

The invention discloses an image watermarking method and system based on decoding guided by a self-supervised visual model. The method comprises the following steps: acquiring watermark information m and an original image I of each data sample in a training data set; for each data sample, preprocessing the watermark information m to generate a message tensor M, and performing degradation distortion processing on the original image I to generate a distortion guide heat map DGH, thereby obtaining a preprocessed training data set; and performing self-supervising training on the self-supervising visual model by using the preprocessed training data set to finally obtain the trained self-supervising visual model. The invention aims to solve the problem that the decoding robustness is poor when the mask prediction is inaccurate due to the fact that the existing printing-resistant shooting watermark technology excessively depends on the positioning mask, and the semantic comprehension capability of the depth vision model is utilized to replace the traditional geometric synchronization, so that the end-to-end robust decoding without correction is realized.

The invention discloses a robust watermarking method for stable diffusion generated images. The robust watermarking method comprises the following steps: step 1, watermark embedding; step 2, generating a watermark image; step 3, watermark extraction; step 4, watermark detection and source tracing; according to the method, the watermark information is encrypted and mapped into Gaussian noise approximately conforming to normal distribution, and is embedded into a potential space instead of directly modifying pixels, so that visual distortion of an image is avoided, native logic of a diffusion model is fit, model parameters do not need to be adjusted or network fine adjustment is not needed, and the original generation performance of the model can be fully guaranteed; the watermark is expanded to the potential space full dimension, the watermark covers the whole semantic level of the image, a voting mechanism is introduced in the extraction stage to carry out consistency verification on the multi-copy watermark, various common attacks can be effectively resisted, the generated image can still keep the complete content structure and semantic consistency even under the high watermark capacity, and the image quality is improved. And the balance of model performance, image quality and robustness is realized.

The invention relates to an implicit black box watermarking method for copyright protection of a multi-modal model, and belongs to the technical field of artificial intelligence safety. The method specifically comprises the following steps: 1, generating a multi-modal model black box backdoor trigger; 2, completing model training of a conversion module according to the backdoor trigger set in the step 1; and 3, performing ownership verification and infringement model judgment on the suspicious model according to the backdoor trigger set in the step 1 and the conversion module in the step 2. According to the method, the resistance trigger is generated by adopting the original samples in the original data set, the limit of an existing distributed external trigger is broken, the method can adapt to a real application scene of a multi-modal model under the condition of small overhead, and the method has good practicability and expandability. In addition, two-stage watermark verification based on a conversion module is adopted, so that the infringement problem caused by hostile attacks can be effectively avoided.

The invention discloses a watermarking method and device suitable for any resolution image input, and the method comprises the steps: generating a fixed low-resolution watermark residual error through a watermark encoder according to an input carrier image and a watermark message, restoring the watermark residual error to the size of the carrier image, and superposing the watermark residual error with the carrier image to obtain a watermark-containing image; designing a watermark discriminator based on the residual neural network, wherein the watermark discriminator is used for distinguishing the difference between the watermark-containing image and the carrier image; a watermark noise layer is used for simulating various distortion operations suffered by an image under a digital channel, and disturbance is applied to the watermark-containing image processed by a watermark discriminator; and transmitting the watermark-containing image to which the disturbance is applied in a real channel, inputting the watermark-containing image into a robust watermark decoder, and decoding and extracting a watermark message. According to the method and the device, image input of any resolution can be processed, good invisibility and robustness can be kept, and copyright protection and leakage traceability of the image of any resolution can be realized.

The invention discloses an image watermark generation method and system based on a diffusion model optimization potential variable, and the method comprises the steps: training a secret encoder and a watermark extractor, and coding a message into a secret residual error; after the residual error is injected into the potential representation of the image, mapping the residual error into a potential vector with a watermark, which accords with the prior of a diffusion model, through DDIM inversion; performing iterative optimization on the potential vector by using a pre-trained diffusion model U-Net, introducing double constraints of image reconstruction loss and message fidelity loss in a denoising process, and generating an image with high visual quality and high watermark robustness; and finally, extracting the message through a watermark extractor and carrying out statistical significance verification to realize copyright authentication. According to the method, dynamic watermark embedding is achieved, the attack resistance is high, the image quality is well kept, an original model structure is not changed, and the method is suitable for copyright protection and user tracking of AIGC content.

This invention discloses a deep learning-based Hadamard transform-based anti-screen-capture watermarking method, belonging to the field of watermarking technology. This invention combines a convolutional neural network (CNN) and residual blocks to achieve an end-to-end process of watermark embedding and extraction within the Hadamard domain. By simulating screen-capture attacks and incorporating this method between the embedding and extraction layers, the network ensures robust watermark embedding. This method can efficiently extract watermark information from photos taken by surreptitiously, protecting the copyright of digital media. The use of the transform domain for watermark embedding allows the watermark to spread over a wider area of ​​the image, significantly improving the robustness of the watermarking algorithm. Comparison with related technologies demonstrates the superiority of this invention in terms of imperceptibility and robustness.

The application relates to the technical field of digital video processing, and discloses a video stream block time evidence watermarking method based on time authentication, which comprises the following steps: receiving a continuous video code stream and dividing the video code stream into multiple continuous video blocks, extracting video coding layer data of the first video block, performing a hash operation to generate a reference hash value; sending the reference hash value to a time watermarking device, performing a digital signature operation on the reference hash value in combination with an absolute timestamp and a signature key to generate a digital signature; intercepting a bit segment from the digital signature as a characteristic sequence to form cascade digest data; and modulating preset macro block syntax elements of the first frame of the next video block by using the cascade digest data, and outputting reconstructed video coding layer data. The method effectively reduces processor occupation and memory consumption of the system, enables video content to meet real-time transmission requirements in a monitoring scene while guaranteeing time sequence and data anti-fake capability.

This invention discloses a lightweight watermarking method for robust image attribution based on latent space diffusion models. The method uses low-dimensional feature vectors as the watermark carrier: first, a feature watermark library is constructed; then, a feature-noise encoder and a noise-feature decoder are trained. The encoder maps the feature watermark to initial latent space noise that satisfies a standard Gaussian distribution and inputs it into the latent space diffusion model to generate an image. When attribution verification is required, the image to be verified is inverted to recover the initial latent space noise, and the corresponding feature watermark is reconstructed by the decoder. The identity is determined using the cosine similarity between the reconstructed watermark and the original watermark in the feature watermark library. This invention maintains the quality of the generated image while exhibiting strong robustness to common distortions such as JPEG compression, cropping, random discarding, blurring, and brightness variations, and significantly reduces the overhead of watermark storage and matching computation.

The invention discloses a three-dimensional grid robust watermarking method and system based on spherical coordinate statistical characteristics, and the method comprises a watermark embedding stage and a watermark extraction stage, in the watermark embedding stage, three-dimensional grid vertexes are converted to a spherical coordinate space, a stable equivalent binning structure is constructed, and statistical characteristics are extracted, so that the three-dimensional grid robust watermarking is realized. And frequency domain transformation is carried out. In combination with a quantization domain binary mapping mode of quantization index modulation, watermark information is embedded into a frequency domain coefficient, and then a three-dimensional model carrying a watermark is generated through inverse transformation and coordinate reduction. The system comprises a coordinate conversion module, a binning module, a feature extraction module, a frequency domain transformation module, a watermark embedding module, an inverse transformation module, a vertex updating module and a model output module. The method provided by the invention has a large capacity up to 256 bits, and has significant robustness under rotation, scaling, translation, smoothing, simplification, vertex reordering and noise attack. The method can be widely applied to the technical field of three-dimensional data processing.

The invention relates to an official content credibility verification-oriented conditional trigger type text watermarking method, which comprises the following steps of: extracting confidence of a large language model, embedding a hierarchical trigger watermark, dividing a pseudo-random red and green set, detecting statistical characteristics and judging thresholding authenticity. Implicit watermark embedding of an official text and rapid discrimination of a public opinion end are realized. According to the method, the natural readability of the official text and the detectability of the watermark are both considered, the detection Z-score can reach 7.49, the green set hit proportion reaches 69.21%, and the method is suitable for the scenes of government affair document release, news draft clarification, network public opinion management and the like, and has good expansibility and engineering practicability.

The application discloses a large model watermarking method based on dynamic entropy, belonging to the field of large model watermarking. It includes: modifying the output token sampling process; dynamically adjusting the size of the sliding window and the size of the candidate token pool according to the probability distribution entropy value of the large model output; through pseudo-random scoring and multi-round competition; for each token in the sequence to be analyzed, the same watermark key and the context before the token are used to recalculate the pseudo-random seed, and based on the seed, the corresponding pseudo-random score of the token at all levels is regenerated; the average pseudo-random score of all tokens in the text is calculated as the aggregate score, and the statistical threshold is determined based on the preset false positive rate. If the statistical threshold is less than the aggregate score of the unknown text, it is determined as a generated text with watermark, otherwise it is a text without watermark. The application realizes high-precision source identification and tracing of large model output content through the dynamic entropy watermarking method while ensuring the original quality of the generated text.

The application discloses a kind of based on thumbnail encryption image's authenticatable user end watermark method, belong to computer security technical field. Including: image pre-processing, image encryption, key generation and distribution, decryption and watermark embedding, tamper detection and positioning, tamper content recovery, extract watermark lock user identity.The present application is based on the holographic redundancy embedding of thumbnail and the fine authentication based on parity check, with very strong anti-shearing and self-healing ability;Using the disturbance of pair of pixel difference value, all image pixels can be embedded watermark, and the security blind area is eliminated;Encrypted image has clear identifiable thumbnail, and is highly similar to plaintext, which is convenient for cloud management, and the details privacy is effectively protected through high-frequency information confusion;Encryption and watermark embedding are fused in bottom algebra operation, and the calculation efficiency is high.

The present disclosure provides a method for embedding watermarks into protein generative models, comprising pretraining an SE(3)-equivariant watermark encoder and decoder, wherein the encoder receives a watermark code and generates a watermark-conditioned structure, and the decoder receives the watermark-conditioned structure and predicts an embedded watermark, and using a watermark-conditioned adaptation to encode a desired watermark code and generate an updated protein generative model by merging the desired watermark code into model weights from an original protein generative model, wherein the protein generative model is fine-tuned with a message retrieval loss and a consistency loss. The watermark-conditioned adaptation includes a gating vector derived from the watermark code. The method enables copyright authentication and tracking of generated protein structures while preserving structural integrity and biological functionality.

This invention discloses a robust watermarking method for medical images based on the Mamba architecture, resistant to multiple attacks. The method includes: generating a watermarked image by processing the original medical image and watermark information through an encoder network, where the encoder includes a hierarchical feature refinement sampler module and a channel-aware Mamba architecture bottleneck layer; recovering the watermark from the potentially attacked watermarked image through a decoder network, where the decoder includes a wavelet domain Mamba module and a sequence context dependency module for dual-path processing; randomly selecting 2-4 attacks from an attack library and applying them to the watermarked image in a random order and intensity using dynamic composite attacks; and end-to-end optimization using a composite loss function containing reconstruction loss, clean decoding loss, robust decoding loss, and perceptual loss. This invention decouples the image frequency band through discrete wavelet transform, embeds the watermark at high frequencies, and utilizes Mamba's selective state-space mechanism to model the long-range dependencies of wavelet coefficients, thereby improving the robustness of the watermark.

This invention discloses an image watermarking method and system based on self-supervised visual model-guided decoding. The method includes acquiring watermark information *m* and the original image *I* from each data sample in the training dataset; for each data sample, preprocessing the watermark information *m* to generate a message tensor *M*, and performing degradation distortion processing on the original image *I* to generate a distorted guided heatmap *DGH*, thus obtaining a preprocessed training dataset; and using the preprocessed training dataset to perform self-supervised training on a self-supervised visual model to finally obtain a trained self-supervised visual model. This invention aims to solve the problem that existing anti-printing watermarking technologies overly rely on location masks, resulting in poor decoding robustness when mask prediction is inaccurate. It utilizes the semantic understanding capability of deep visual models to replace traditional geometric synchronization, achieving end-to-end, correction-free robust decoding.

There is provided an object attribute-based watermarking method for preventing leakage of a digital content. A video watermarking method according to an embodiment may acquire a requested video and watermarking information upon receiving a request for video streaming from a user, may determine some of objects appearing in the video according to the watermarking information, may transform attributes of the determined objects, and may stream the transformed video. Accordingly, by watermarking an attribute of an object appearing in a video differently according to a user and providing the video, a watermark may be difficult to perceive, robustness against various attacks may be guaranteed, and a leakage path of a content may be checked.

This invention relates to the field of digital image security technology, and in particular provides a robust feature embedding image watermarking method based on noise-aware guidance. The method includes acquiring a carrier image dataset, constructing a robust feature embedding image watermarking network based on noise-aware guidance, the robust feature embedding image watermarking network including a watermark embedding architecture and a watermark extraction architecture; inputting the carrier image into the watermark embedding architecture to obtain a watermarked image; inputting the watermarked image into the watermark extraction architecture to obtain watermark information; optimizing network parameters, and simultaneously using a discriminator as an adversarial network, utilizing adversarial loss to ensure that the carrier image and the watermarked image are similar. This method improves robustness against attacks while generating high-quality watermarked images.

The application discloses a robust high-quality three-dimensional model digital watermark embedding and extracting method, the watermark embedding method comprises the following steps: filtering a vertex order initial point by using a three-dimensional grid model vertex in-degree; calculating a robust starting point and a vertex order which are not influenced by three-dimensional coordinate values of the vertex by using a sum of geodesic distances of the vertex; dividing the vertex into embeddable watermark points and reference points by using a specific algorithm; and embedding a watermark in a local space of the embeddable watermark points by using a principal component analysis and a quantization method. The application also proposes a watermark extracting method corresponding to the watermark embedding method. Compared with a traditional space domain three-dimensional grid model watermark method, the watermark embedding method has smaller distortion caused to a model surface, stronger anti-noise attack capability and more robust vertex order corresponding to the model.

The invention discloses a remote sensing image robust watermarking method based on an improved reversible neural network, and relates to the technical field of digital image copyright protection, and the method comprises the steps: collecting an original remote sensing image and a binary watermark sequence, and carrying out the preprocessing, and forming a training sample; inputting the training sample into the watermark model; applying randomly selected noise attack to the watermark image through a noise layer to generate a disturbed watermark image; inputting the disturbed watermark image into the watermark model, and executing a reverse extraction path to obtain extracted watermark information; calculating a multi-target loss function, wherein the multi-target loss function is based on the original remote sensing image, the binary watermark sequence, the watermark image and the extracted watermark information; and training the model until convergence. Through a robust lifting mechanism and an improved reversible neural network structure, the method shows extremely high stability to various complex noise attacks, and has good generalization ability and environmental adaptability.

The application discloses an image watermark embedding and extracting method, device and equipment and a readable storage medium, and belongs to the technical field of multimedia content security. The application obtains first watermark information and an original picture, converts the first watermark information and the original picture into two-dimensional watermark features and original picture frequency domain features, inputs the two-dimensional watermark features and the original picture frequency domain features into an invertible neural network in a forward direction, and outputs a watermark picture and loss information; converts the watermark picture and the loss information into watermark picture frequency domain features and a matrix, inputs the watermark picture frequency domain features and the matrix into the invertible neural network in a reverse direction, and outputs second watermark information; and compares the first watermark information with the second watermark information to obtain a similarity verification result. The similarity verification result is used to determine the copyright ownership of the original picture. In combination with a discrete wavelet transform, watermark message processing and the invertible neural network, a high-robustness deep learning image watermark method is realized on the basis of ensuring image quality.

The invention provides a fast generation type screen shooting robust watermarking method and system based on structure perception. The method comprises the steps that 1, original watermarking information is mapped to a high-dimensional continuous submerged space with redundancy characteristics; step 2, constructing a condition-guided watermark residual error generation network, directly mapping the redundant watermark vector to a distribution space of watermark residual error signals under the constraint of image structure prior, and realizing combined control of the amplitude and spatial distribution of the watermark residual error signals; step 3, fast watermark extraction is realized through a multi-stage down-sampling and channel constraint mechanism; and step 4, combining a plurality of continuous illumination field generation mechanisms with an imaging distortion model to construct illumination disturbance of a multi-scene combination. According to the method, the watermark embedding process is converted from the direct optimization of the watermark image domain to the structured modeling of the generative watermark residual error, and the robustness under the screen shooting condition is improved while the calculation complexity is remarkably reduced.