27 results about "Jpeg compression" patented technology

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.

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 provides a residual DCT (Discrete Cosine Transformation) feature guided artifact learning module and an image tampering detection system, and the module comprises a DCT coefficient processing unit, a quantization table processing unit, a residual DCT calculation unit, a feature splicing unit and a neural convolution processing unit. The residual DCT calculation unit is used for performing repeated recompression on DCT coefficient matrix simulation JPEG compression to obtain DCT residual information and construct a residual DCT feature map on the basis of the DCT residual information, and the DCT coefficient processing unit is used for encoding the DCT coefficient matrix into DCT plane binary volume representation by using threshold truncation and One-Hot encoding, and then performing feature extraction and processing; the quantization table processing unit is used for expanding and processing a quantization table; the feature splicing unit is used for feature splicing; and the neural convolution processing unit is used for processing the spliced features to obtain final DCT features. According to the method, the positioning and identification capability of the tampered area in the double JPEG image with the same QF compressed twice is effectively improved.

The application provides a method for detecting synchronous JPEG recompression of BMP format concealed images, and a method for effectively estimating a JPEG quantization table is provided; by using the particularity of JPEG 100 quality factor compression, the minimum error caused by JPEG compression when the quality factor is 100 is extracted to construct an expected-minimum error feature; by using the convergence characteristics of inverse quantization JPEG coefficients, a first-order relative error feature is proposed, and finally synchronous JPEG recompression images saved in BMP format are effectively detected, and synchronous JPEG recompression at any quality factor can also be detected. The application can effectively distinguish single compressed images from recompressed images, and has higher accuracy compared with corresponding traditional JPEG recompression detection methods.

The invention discloses a JPEG compression verification method and device and a storage medium, and relates to the field of data processing, and the method comprises the steps: obtaining an original image frame and a compressed image; performing gray processing and image cutting operation on the compressed image to obtain a plurality of compressed image blocks and gray image blocks; performing edge detection on the grayscale image block to obtain a binary edge image; obtaining an affine transformation matrix according to pixel values of all pixels in the binary edge image; generating a plurality of feature image blocks according to the original image frame, the image configuration parameters, each compressed image block and the affine transformation matrix; performing feature comparison on the ith compressed image block and the ith feature image block to obtain an ith similarity comparison result; and comparing the ith similarity comparison result with a pre-configured preset threshold value to obtain an ith comparison result, and when the obtained i comparison results meet a preset stop condition, obtaining a compression verification result. The method is high in compatibility, low in cost and high in efficiency.

The invention provides a JPEG (Joint Photographic Experts Group) compression artifact learning module and an image tampering detection system, and the JPEG compression artifact learning module comprises a DCT (Discrete Cosine Transform) coefficient processing unit, a quantization table processing unit, a feature splicing unit and a neural convolution processing unit, the DCT coefficient processing unit is used for coding a DCT coefficient matrix into DCT plane binary volume representation by using threshold truncation and One-Hot coding, and then performing feature extraction and processing to obtain a first feature map; the quantization table processing unit is used for expanding and processing the quantization table to obtain a second feature map; the feature splicing unit is used for splicing the first feature map and the second feature map; and the neural convolution processing unit is used for performing channel compression, normalization and activation on the spliced feature vectors to obtain final DCT features. According to the method, the CNN can directly learn a local JPEG compression artifact mode, and the tampering detection efficiency and precision are improved.

The application discloses a deep image inpainting tampering detection method based on adaptive tampering trace learning, and belongs to the technical field of digital image forensics. The method first processes the input image by using an adaptive differential convolution module to suppress image content and enhance tampering traces; then, multi-scale and fine-grained features are extracted in parallel by a multi-scale hollow convolution module and a dense connection network; next, a neural network structure search module is used to automatically optimize the feature extraction path to adapt to diversified tampering types; then, a global and local double-branch attention enhancement module is used to fuse multi-level features and simultaneously improve the internal consistency and boundary accuracy of the tampering area; finally, a decoder module is used to output a pixel-level tampering area mask. The application can adaptively learn tampering features and maintain high precision and strong robustness under post-processing conditions such as JPEG compression, scaling, noise addition and the like, and is suitable for fields such as digital forensics and media content security.

The invention relates to a diffusion model generated image detection method and device, and a storage medium. The method comprises the following steps: S1, obtaining an original input image and a label value thereof; s2, reconstructing the original input image for N times to obtain N generated images, and forming a basic image sequence by the original input image and all the generated images; s3, deleting a first image of the basic image sequence to obtain a first enhanced image sequence; s4, performing linear combination on every two adjacent images in the basic image sequence to obtain second generated images, and arranging all the second generated images in sequence to obtain a second enhanced image sequence; s5, deleting the last image in the basic image sequence to obtain a reference image sequence; s6, performing feature extraction on each image sequence to obtain a respective feature vector; s7, training the detection model by using the feature vectors and label values of the first enhanced image sequence, the second enhanced image sequence and the reference image sequence; and S8, carrying out authenticity detection on a to-be-detected image by using the trained detection model. Compared with the prior art, the method has the advantages that higher detection precision can be realized under the condition of less training data, the average accuracy on multiple reference sets is remarkably improved, and the method has robustness on JPEG compression and zooming.

The invention discloses a depth image restoration tampering detection method based on adaptive tampering trace learning, and belongs to the technical field of digital image forensics. The method comprises the following steps: firstly, processing an input image by using a self-adaptive differential convolution module so as to inhibit image contents and enhance tampering traces; then, multi-scale and fine-grained features are extracted in parallel through a multi-scale cavity convolution module and a dense connection network; then, automatically optimizing a feature extraction path by using a neural network structure search module to adapt to diversified tampering types; then, through an attention enhancement module containing global and local double branches, fusing multi-level features and simultaneously improving internal consistency and boundary precision of a tampered region; finally, a pixel-level tampered region mask is output by the decoder module. According to the method, tampering features can be adaptively learned, high precision and high robustness are kept under post-processing conditions such as JPEG compression, zooming and noise adding, and the method is suitable for the fields of digital forensics, media content security and the like.

The application discloses a kind of based on model integration's deep counterfeit detector confrontation robustness evaluation method, belong to computer vision and information security technical field.For the problem that existing deep counterfeit detector confrontation sample migration is poor, compression robustness is insufficient, the application generates confrontation sample by integrating multiple deep counterfeit detection models of different structures using momentum iteration-common weak point attack algorithm, while embedding differentiable JPEG compression module to simulate real compression process.The technical scheme includes: selecting multiple counterfeit detection models to construct evaluation sample set;generate basic confrontation sample using model integration strategy;generate compression robust confrontation sample in combination with differentiable compression;system evaluates the attack effect of confrontation sample under different compression intensities.The application improves the migration and compression robustness of confrontation sample, and can comprehensively evaluate the security vulnerabilities of deep counterfeit detection system.

The invention discloses a video image compression and decompression method and device, equipment, a medium and a product, relates to the technical field of image processing, is applied to a data sending end, and comprises the following steps: obtaining current frame quantized data obtained by performing quantization processing on a current frame image based on a JPEG compression algorithm, and obtaining previous frame quantized data from a first preset memory; the previous frame of quantized data is data obtained after a previous frame of image is quantized based on a JPEG compression algorithm; performing difference value calculation on the current frame of quantized data and the previous frame of quantized data to obtain difference value quantized data, and performing encoding processing on the difference value quantized data based on a preset encoding algorithm to obtain a difference value encoding image; and adjusting the format of the difference coding image based on an image format corresponding to the JPEG compression algorithm to obtain a target compressed image, and transmitting the target compressed image to a data receiving end. The inter-frame data comparison is realized, and the data volume in the image transmission process is reduced.

The application discloses a JPEG image compression method and system based on double Markov chain optimization, relates to the technical field of image compression, and comprises a receiving / acquiring unit, an activity level analysis unit, a state updating unit, a Markov prediction unit, a JPEG compression unit and an output processed image unit. The system can multi-dimensionally analyze user activity level, dynamically update a state transition matrix, adopt a multi-Markov chain model to predict a quality factor, so that the best balance between image quality and compression efficiency is found, resource waste or poor image quality caused by fixed parameter compression is avoided, appropriate compression decisions can be made by virtue of multi-source data, system state is dynamically updated, changes of system environment and user behavior are responded in time, and image quality meeting user expectation is provided according to user operation on images and activity level.

This application discloses a dual-channel image acquisition and processing method and a local on-orbit programming method based on a System-on-Chip (SOC). A first SOC processing unit acquires visible light images, and a second SOC processing unit acquires short-wave infrared images; both undergo preprocessing, target extraction, and JPEG compression. Both processing units utilize heterogeneous SOC chips with integrated RISC-V CPUs and FPGAs, and are independently equipped with DDR3 and SPI NOR Flash. The FPGA integrates connected component labeling and centroid extraction modules, employing a single-scan algorithm for parallel processing and directly outputting centroid coordinates at the end of a single frame scan. Local on-orbit programming uses function-level BIN splitting and CRC comparison, only programming the change modules. JPEG compression adjusts image parameters through configuration registers and automatically reloads the quantization table. This application achieves parallel acquisition of visible and infrared light, real-time centroid extraction, incremental programming, and flexible compression, suitable for aerospace attitude control and remote sensing imaging.

The application discloses a self-adaptive JPEG compressed image encryption method device, equipment and medium, wherein the method comprises the following steps: acquiring a JPEG image and a user key K init ; adaptively generating three keys according to the JPEG image and the user key K init ; generating three pseudo-random number sequences according to the three keys; decoding the JPEG image to obtain DC coefficients and AC coefficients; encrypting the DC coefficients according to the first pseudo-random number sequence; encrypting the AC coefficients according to the second pseudo-random number sequence; and encoding the encrypted DC coefficients and AC coefficients to obtain an encrypted JPEG image. The application only needs the user to provide the own initialization key K init , and adaptively generates the random number key required in the encryption scheme according to the content of the JPEG image itself, so that the key of each image to be encrypted is different, and the security is improved. The application can be widely applied to the field of image information security.

The application discloses a kind of JPEG enhanced two-stage watermark embedding method and system, method includes: constructing the watermark model including encoder, decoder, discriminator and noise layer;Optimal watermark model is obtained using two-stage training strategy, in the joint training of encoder and decoder in the first stage, introduce JPEG noise layer, in the second stage, fix the encoder and fine-tune the decoder using multiple noise attacks;Wherein, the encoder extracts features through the multi-path global-local aggregation module, and uses the channel-space collaborative attention mechanism in the redundancy enhancement module, and fuses the frequency domain statistical information to optimize the embedding position;After the carrier image to be embedded with watermark is preprocessed, it is input into the watermark model loaded with optimal model weight together with watermark information, and the corresponding watermark image is output. The application effectively improves the robustness of the watermark to JPEG compression and other attacks, while ensuring the high visual quality of the watermark image.

The present application relates to a kind of based on halftone noise and artifact enhancement document image tampering positioning method.The method constructs halftone noise perception stream, utilizes denoising residual mechanism to extract the micro halftone statistical law damaged by tampering;Frequency domain perception stream is constructed, decouples luminance and chroma in YCrCb color space, removes low-frequency background by combining adaptive frequency domain enhancement and adaptive high-pass filter, and accurately captures high-frequency quantization artifact;Covariance-based feature fusion mechanism is constructed, spatial domain, frequency domain and halftone noise domain features are cascaded and fused, and the conflict of multi-modal heterogeneity is relieved to accurately locate tampering area.The method is for the authenticity verification scene of document image disturbed by complex post-processing such as JPEG compression, scaling, and good results are obtained in positioning accuracy and robustness.

The application discloses a JPEG compression verification method and device and a storage medium, relates to the field of data processing, and comprises the following steps: acquiring an original image frame and a compressed image; performing grayscale processing and image cutting operation on the compressed image to acquire a plurality of compressed image blocks and grayscale image blocks; performing edge detection on the grayscale image blocks to acquire a binary edge image; acquiring an affine transformation matrix according to the pixel values of all pixels in the binary edge image; generating a plurality of feature image blocks according to the original image frame, image configuration parameters, each compressed image block and the affine transformation matrix; performing feature comparison on the i-th compressed image block and the i-th feature image block to acquire an i-th similarity comparison result; comparing the i-th similarity comparison result with a preconfigured preset threshold value respectively to acquire an i-th comparison result; and acquiring a compression verification result when the i acquired comparison results meet a preset stop condition. The method has high compatibility, low cost and high efficiency.

The application provides a JPEG compression artifact learning module and an image tamper detection system, and the JPEG compression artifact learning module comprises a DCT coefficient processing unit, a quantization table processing unit, a feature splicing unit and a neural convolution processing unit, wherein the DCT coefficient processing unit is used for encoding a DCT coefficient matrix into a DCT plane binary volume representation by using threshold truncation and One-Hot coding, and then performing feature extraction and processing to obtain a first feature map; the quantization table processing unit is used for expanding and processing a quantization table to obtain a second feature map; the feature splicing unit is used for splicing the first feature map and the second feature map; and the neural convolution processing unit is used for performing channel compression, normalization and activation on the spliced feature vector to obtain a final DCT feature. The application enables the CNN to directly learn local JPEG compression artifact patterns, and improves tamper detection efficiency and accuracy.

The application discloses a digital watermark embedding and extracting method, device and equipment and a storage medium. The original image and the digital watermark image are acquired; at least one watermark embedding area of the original image is determined, and the concealment of the digital watermark is improved; based on the fact that the JPEG compression usually retains a higher sampling rate for the luminance component, the luminance of the pixel points in each preset pixel area which is not overlapped in the watermark embedding area is adaptively adjusted in sequence through an adaptive luminance adjustment algorithm, so that the digital watermark image is embedded in the watermark embedding area to obtain a digital watermark embedding image, and the digital watermark embedding image has good robustness to the joint attack of the screenshot and the JPEG compression saving.

The invention discloses a JPEG (Joint Photographic Experts Group) image encoding and decoding method and device and a computer program product, and relates to the technical field of image processing. The coding method comprises the following steps: carrying out block processing on a to-be-coded image; extracting features of each image block; clustering the image blocks into a limited number of categories based on the features; for each category, generating a quantization matrix of the category according to the frequency domain coefficient characteristics of the image blocks of the category; and according to the category to which each image block belongs, calling a corresponding quantization matrix for quantization, and generating a coding code stream containing the index information and each category of quantization matrix data. The decoding method comprises the following steps: analyzing a code stream to obtain index information and quantization matrix data; and determining a corresponding quantization matrix for each image block according to the index information to perform inverse quantization and image reconstruction. According to the method, the quantization matrixes corresponding to the categories are clustered, so that the JPEG compression efficiency is remarkably improved, the extra storage and code stream overhead is effectively controlled, and good compatibility is kept.

The application relates to a device for carrying out fixed code rate JPEG format image compression, characterized by comprising an image acquisition module, an image compression parameter generation module and a JPEG compression module; the image acquisition module is used for loading a to-be-compressed image for fixed code rate JPEG format compression; the image compression parameter generation module is used for calculating corresponding compression parameters according to the input to-be-compressed image and outputting the compression parameters to the JPEG compression module; the JPEG compression module is used for compressing the to-be-compressed image according to the input compression parameters and outputting a fixed code rate JPEG format compressed image; wherein a support vector machine trained and learned is adopted in the image compression parameter generation module to classify the input to-be-compressed image to obtain the compression parameters corresponding to the to-be-compressed image. The application can guarantee the compression rate of the compression result of the image compression algorithm, consider the image transmission efficiency and the quality of the compressed image, and maximally reduce the loss of the image quality.

The invention discloses a JPEG (Joint Photographic Experts Group) image compression method and system based on double Markov chain optimization, and relates to the technical field of image compression, and the system comprises a receiving / collecting unit, an activity level analysis unit, a state updating unit, a Markov prediction unit, a JPEG compression unit and an output processing image unit. The system can analyze the activity level of a user in multiple dimensions, dynamically update a state transition matrix and predict a quality factor by adopting a multi-Markov chain model, so that the optimal balance between the image quality and the compression efficiency is found, resource waste or poor image quality caused by fixed parameter compression is avoided, and the user experience is improved. A proper compression decision can be made by means of multi-source data, and by dynamically updating the system state, the change of the system environment and the user behavior is responded in time, and according to the operation and activity level of the user on the image, the image quality meeting the expectation of the user is provided.

The application provides a JPEG image restoration method based on a discrete cosine transform and a Mamba network, which is constructed by constructing a DCT Mamba network architecture, combining the long sequence modeling advantage of the Mamba with the characteristic that high-frequency components are discarded after JPEG compression, introducing a discrete cosine transform into the network, establishing a causal relationship sequence conforming to the autoregressive scanning characteristic of the Mamba, improving the rationality of the Mamba for image restoration, and proposing a scale adaptive normalization method to ensure consistent restoration of different image sizes. The application can establish a scanning sequence conforming to the autoregressive characteristic of the Mamba, and can flexibly process pictures of different sizes, thereby improving the fidelity of JPEG image restoration.

The invention discloses a perception-attention image compression model for a power transmission line inspection image, and the model comprises a region of interest segmentation module which obtains depth information of the power transmission line inspection image, and segments a region of interest through the depth difference of the region; the self-adaptive compression module is used for realizing efficient compression through double operations through sparse sampling-interpolation fitting and improved JPEG compression, and avoiding JPEG distortion at the same time; the quality monitoring module is used for dynamically adjusting compression parameters by setting double SNR observation points so as to improve the quality and the compression rate of the compressed image; and the differential compensation module is used for carrying out residual enhancement on the region of interest. The overall compression efficiency is improved through sparse sampling and interpolation fitting technologies, the image quality of a region of interest is ensured in combination with a signal-to-noise ratio observation point and a differential compensation mechanism, the region of interest is accurately identified and segmented in cooperation with a region of interest segmentation algorithm based on depth information, and the purposes of improving the compression rate and protecting the image quality of the region of interest are achieved.

The application belongs to the technical field of image processing, and discloses a JPEG image compression method, device, medium and equipment for target detection, comprising processing a disclosed target detection data set to obtain a regression sample set; selecting an image feature associated with JPEG image compression in the regression sample set, and calculating an image feature vector; based on the obtained regression sample set and image feature vector, performing regression on an optimal quality factor, training a regression model, obtaining a regression parameter, and compressing an input image through the regression model. The application calculates an optimal quality factor based on a JPEG compression regression algorithm without reducing the calculation index of a downstream visual task, so as to adaptively compress an image and reduce resource consumption in a transmission process.

The invention discloses a JPEG (Joint Photographic Experts Group) compressed image tampering positioning method based on a self-supervised data enhancement strategy. The method comprises a compression proxy network based on a double-attention mechanism and a double-domain multi-attention tampering positioning network; wherein the compression proxy network based on the double attention mechanism is trained by utilizing self-supervised learning; and the double-domain multi-attention tampering positioning network is trained by using the tampered image after data enhancement. Aiming at the defect of the existing method in anti-JPEG compression robustness, a compression proxy network based on a double-attention mechanism is designed, a self-supervised learning normal form is used for training, and an improved JPEG proxy image with a universal JPEG compression characteristic is generated, so that data enhancement is realized; on the basis, an improved JPEG proxy image is used for training a double-domain multi-attention tampering positioning network, and the tampering positioning accuracy under JPEG compression interference is improved.

The invention relates to a pseudo-static random access memory write-in performance optimization method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps of receiving JPEG compressed data and performing hardware decoding, writing decoded pixel data into an intermediate cache of an on-chip static random access memory according to a preset granularity, and dividing the intermediate cache into at least two logic partitions; generating a direct memory access transmission parameter aiming at each resolution and each format, a static random access memory source address fixed table and a continuous destination address mapping table of a pseudo-static random access memory frame cache; and starting direct memory access to carry data between the intermediate cache and the pseudo-static random access memory in a continuous burst mode, and controlling logic partitions of the intermediate cache to alternately receive decoded pixel data in a ping-pong mode and carry the decoded pixel data for direct memory access until all decoding and carrying processing of the JPEG compressed data is completed. By adopting the method, the write-in bandwidth utilization rate and the decoding performance can be improved.