100results about "Image watermarking" patented technology

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 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 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 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 systems and methods disclosed herein embed and store traceable watermarks across multiple data modalities including text, image, audio, video, and structured data. The systems and methods disclosed herein receive an input dataset and generate unique watermark patterns for each data modality using cryptographic hash functions applied to dataset identifiers. The watermark patterns include parameters that provide instructions to watermark the input dataset. Watermarking includes modifying discrete data values, relationships, or arrangements within the original dataset at predetermined locations. The watermarked dataset is used to generate a hierarchical fingerprint structure including multiple levels of cryptographic hash identifiers that represent different portions of the data (e.g., at varying granularities). Both the watermark patterns and hierarchical fingerprints are stored in a distributed database accessible through a decentralized network, thus enabling authorized computing devices to verify data provenance and detect potential misappropriation across heterogeneous digital content types.

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 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 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 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 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.

Systems and methods of the present disclosure are directed to a computing system. The computing system can obtain a message vector and video data comprising a plurality of video frames. The computing system can process the input video with a transformation portion of a machine-learned watermark encoding model to obtain a three-dimensional feature encoding of the input video. The computing system can process the three-dimensional feature encoding of the input video and the message vector with an embedding portion of the machine-learned watermark encoding model to obtain spatial-temporal watermark encoding data descriptive of the message vector. The computing system can generate encoded video data comprising a plurality of encoded video frames, wherein at least one of the plurality of encoded video frames includes the spatial-temporal watermark encoding data.

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.

The invention discloses a face deep counterfeiting-oriented robust countermeasure watermark generation method, device and equipment, and relates to the technical field of deep counterfeiting defense. The method comprises the following steps: inputting an original face image into a forward diffusion process of a denoising diffusion implicit model to obtain a noise potential variable, and executing a reverse denoising process; in each denoising step, a current intermediate image is generated, then the loss of counterfeit output is calculated, gradient information is obtained according to the loss and added to the current intermediate image, and an intermediate noise image is obtained. And a denoising step is executed until a complete denoising process is completed, an intermediate step adversarial image is obtained, a post-processing image is obtained through noise layer processing, then mean square error loss of counterfeit output and structural consistency loss of the intermediate step adversarial image and the original face image are calculated, and a total loss function is obtained through weighted fusion. And based on the total loss function, gradient information is obtained and added to the noise potential variable, and a new round of denoising is carried out. And finally, outputting an intermediate step confrontation image of the last iteration.

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.

The invention particularly relates to an AI auxiliary diagnosis method for oral cavity observation, which relates to the technical field of oral cavity medical AI and medical image security, and comprises the following steps: generating a Hash watermark by using patient information, and embedding the watermark into an intermediate frequency alternating current coefficient of a diagnosis core area by adopting a coefficient relation modulation method. According to the method, the local terminal is subjected to deterministic partitioning and frequency domain irreversible desensitization processing, rigid protection of biological privacy of a patient is achieved from the source, and the risk of privacy leakage in oral medical image transmission and storage is dealt with; color space conversion, threshold screening and mathematical morphology operation are completed at a local terminal, a diagnosis core area and a privacy sensitive area are accurately divided, and privacy processing can be completed without uploading an original image; the privacy sensitive area is subjected to combined processing of direct current component retention, medium and low frequency coefficient pseudo-random attenuation replacement and high frequency coefficient zero setting, a reverse mapping table and attenuation factors are not retained, irreversible blinding is achieved on the mathematical level, and biological characteristics such as cheilogramma and facial skin cannot be restored.

The invention discloses a remote sensing image sensitive target safe distribution method based on zero watermarking and a block chain, and relates to the technical field of remote sensing image processing. The method comprises the steps of extracting a sensitive target image from an original remote sensing image according to a mask, and repairing a sensitive area to generate a published image. Normalized published images and effective areas are determined, and robust feature maps are extracted from the effective areas. And respectively scrambling the sensitive target image and the robust feature map, and then carrying out XOR to generate a zero-watermark image. And packaging the zero-watermark image, the effective area coordinates and the repair algorithm parameters into a record file, encrypting the record file, and then uploading the record file, the published image and the zero-watermark image to a distributed storage to obtain an index. And segmenting the secret key of the record file, and writing the index and secret key segmentation information into a block chain storage certificate. And when the sensitive target needs to be recovered, the authorized user acquires data and a secret key according to the block chain record, reproduces a feature extraction process from the to-be-verified image according to the effective region coordinates and the algorithm parameters, and recovers a sensitive target image through inverse operation in combination with the zero-watermark image.

This invention discloses a secure and robust adversarial visible watermark generation method that is resistant to removal. It obtains the original clean image and original watermark by reading a dataset, and inputs the original clean image into the Grad-CAM++ heatmap network to output a heatmap with location information. The obtained heatmap with location information and the original watermark are scaled and positioned to generate a location mask. Using the location mask and an alpha mixing method, the clean watermark is embedded into the host image to generate initial adversarial examples. The initial adversarial examples are iteratively optimized to generate and embed adversarial visible watermarks. This invention improves the security and robustness of traditional methods. Compared to traditional visible watermarking schemes, this method provides more secure protection for watermarks and copyrights, and has significant practical value.

The present disclosure relates generally to image signal processing including digital watermarking. Digital watermarking can be used to prevent counterfeiting by binding or tying together digital imagery, products depicted in the digital imagery, and website hosting address information associated with the digital imagery. Various combinations and technology are described in this patent document and claims.

The invention discloses a robust image watermarking method based on a fast and high-precision Pseudo-Zernike moment. According to the method, the defects of the existing orthogonal moment watermarking algorithm in precision and calculation efficiency are overcome, the copyright of the digital image is effectively protected, and meanwhile, the resistance of the watermark to common image processing attacks and geometric attacks is improved. The method specifically comprises the following steps: (1) adopting a polar coordinate pixel division and nearest neighbor interpolation algorithm, deducing an analytical solution of a Pseudo-Zernike moment in combination with a Newton-Leibniz formula, and calculating a rapid and high-precision Pseudo-Zernike moment by utilizing symmetric and antisymmetric characteristics of a primary function; (2) screening Pseudo-Zernike moments meeting conditions, carrying out normalization processing, encrypting a watermark sequence by adopting tent chaotic mapping, and embedding the encrypted watermark into the amplitude of the normalization moment through an improved quantitative modulation strategy to obtain a moment value of the embedded watermark; and (3) carrying out inverse Pseudo-Zernike moment transformation on the moment embedded with the watermark, reconstructing to obtain an image with the watermark, and carrying out range correction on a pixel value to generate a final watermark image.

Systems and methods of the present disclosure are directed to a computing system. The computing system can obtain a message vector and video data comprising a plurality of video frames. The computing system can process the input video with a transformation portion of a machine-learned watermark encoding model to obtain a three-dimensional feature encoding of the input video. The computing system can process the three-dimensional feature encoding of the input video and the message vector with an embedding portion of the machine-learned watermark encoding model to obtain spatial-temporal watermark encoding data descriptive of the message vector. The computing system can generate encoded video data comprising a plurality of encoded video frames, wherein at least one of the plurality of encoded video frames includes the spatial-temporal watermark encoding data.

In order to overcome the limitation of a traditional transform domain watermarking technology in the aspect of resisting traditional attacks and geometric attacks, the invention discloses a robust watermarking scheme based on secondary division and three-dimensional quantization, a selected image block is subjected to secondary division to obtain an upper left sub-image block, a lower left sub-image block and a lower right sub-image block, then DC coefficients of the three sub-blocks are directly calculated, and a robust watermarking algorithm is established. And watermark quantization index modulation in a three-dimensional space is carried out to realize watermark embedding, so that the resistance to noise interference and geometric attack is enhanced. The method not only has good invisibility and robustness, but also can effectively solve the problem of weak digital watermark resistance of the color image, and is suitable for occasions of digital media copyright protection.

Systems and methods of the present disclosure are directed to a computing system. The computing system can obtain a message vector and video data comprising a plurality of video frames. The computing system can process the input video with a transformation portion of a machine-learned watermark encoding model to obtain a three-dimensional feature encoding of the input video. The computing system can process the three-dimensional feature encoding of the input video and the message vector with an embedding portion of the machine-learned watermark encoding model to obtain spatial-temporal watermark encoding data descriptive of the message vector. The computing system can generate encoded video data comprising a plurality of encoded video frames, wherein at least one of the plurality of encoded video frames includes the spatial-temporal watermark encoding data.

The invention discloses a revocable depth model watermarking method and system, belongs to the technical field of artificial intelligence model security, is provided for adapting to compliance and flexible requirements of a real industrial scene, and has the technical key points that smooth geometric disturbance is introduced into an intermediate feature space to generate a trigger sample; the original task and the watermarking behavior are restrained simultaneously by adopting joint loss; a watermark sensitive channel is isolated from a normal reasoning channel through structured regularization and routing separation, and interference of watermarks on a main task is reduced; and in the revocation stage, selective gating suppression is carried out on the triggering sensitive neurons, so that controllable, rapid and low-loss watermark removal is realized. In the verification stage, the watermark success rate is calculated by adopting a secret trigger set to judge the watermark existence; in the revocation stage, the WSR is reduced to be below a safety threshold value on the premise of keeping the model precision basically unchanged. Compared with an existing irreversible parameter-level or behavior-level watermark, the method has the advantages of revocation, small residual trace, low influence on main task performance, adaptation to common compression / fine adjustment scenes and the like, is suitable for application scenes such as model short-term authorization, transfer transaction and multi-party cooperation, and can be expanded to various network structures and task types.

Embodiments of the invention include ascribing a predetermined data record to a physical surface in the form of an imperfect aesthetic pattern of marks. An image showing a portion of the surface, when processed with a decoder, yields the predetermined, verified data record.

The application discloses a zero watermark generation method and device, equipment, medium and product, and the method comprises the following steps: constructing a first character quantity set according to the quantity of each key character in the code data to be protected, wherein the key character is a single character defined in advance; constructing a first feature matrix according to the size relationship of different elements in the first character quantity set; and generating a zero watermark image based on a preset watermark image and the first feature matrix. The embodiment of the application realizes deep adaptation with the characteristics of the code data, does not need to embed the code to be protected, does not modify the code file, and can avoid destroying the original business logic of the code.

The application discloses an anchor point synchronous fountain code anti-cropping text watermarking method for a screen camera scene, and specifically comprises character instance extraction and carrier construction, watermark load construction and blocking, data block generation and in-block error correction, anchor point period mapping and character instance level embedding, screen camera and cropping attack, watermark extraction and anchor point synchronous block cutting, data block screening and two-section fountain recovery, and global verification output. The application can realize reliable recovery of watermark information under screen camera degradation and local cropping superposition conditions.