55 results about "Jpeg compression" patented technology

The invention relates to a face depth forgery active defense method and device based on discrete wavelet transform, and relates to the technical field of image processing, and the method comprises the steps: carrying out the attribute editing of an original image and a disturbance-containing image through a depth forgery model, calculating the mean square error loss and structural similarity loss between the original image and the disturbance-containing image, different weights are endowed and fused into a total loss function; gradient information is obtained based on loss function back propagation, the optimal direction of disturbance application is determined, and an intermediate disturbance value is formed; low-frequency and high-frequency components are embedded into corresponding components of the disturbance-containing image through discrete wavelet transform, and then the image is reconstructed through inverse discrete wavelet transform; checking whether the number of iterations is reached or not, and if not, introducing a JPEG noise layer to continuously iterate and adjust disturbance; and if yes, outputting a final face image containing invisible disturbance information. According to the method, the robustness in the wavelet domain is fully utilized, and the resistance of disturbance to JPEG compression is improved on the premise of ensuring that the disturbance is invisible.

Systems and methods are provided for encoding and decoding images using differentiable JPEG compression, including converting images from RGB color space to YCbCr color space to obtain a luminance and chrominance channels, and applying chroma subsampling to the chrominance channels to reduce resolution. The YCbCr image is divided into pixel blocks and a DCT is performed on the pixel blocks to obtain DCT coefficients. DCT coefficients are quantized using a scaled quantization table to reduce precision, and quantized DCT coefficients are encoded using lossless entropy coding, forming a compressed JPEG file decoded by reversing the lossless entropy coding to obtain quantized DCT coefficients, which are dequantized using the scaled quantization table to restore the precision. The dequantized DCT coefficients are converted back to a spatial domain using an IDCT, the chrominance channels are upsampled to original resolution, and the YCbCr image is converted back to the RGB color space.

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 present invention discloses an image restoration forensics method based on feature enhancement neural network, which pre-processes images that have been processed by traditional restoration algorithms, and constructs training sets and test sets based on the pre-processed images and corresponding labels; constructs feature enhancement network modules based on high-pass filters and convolution operations; builds downsampling and upsampling networks; uses the constructed training sets and corresponding label sets to train the designed models; uses the saved best model weights to predict the images of the test set to find the restored areas in the images. The present invention demonstrates the most advanced performance of traditional restoration algorithm forensics, can accurately and effectively locate the restored areas in digital images, and is robust to post-processing operations such as JPEG compression, rotation and scaling.

The invention discloses a joint optimization method for a joint photographic experts group (JPEG) quantization table and a truncation position based on rate distortion, relates to the technical field of JPEG coding optimization, and solves the technical problems that in a JPEG compression scheme, improvement of coding efficiency caused by joint optimization by using the quantization table and the truncation position is neglected, and the optimal quantization table and the optimal truncation position are difficult to find. According to the method, joint optimization is carried out on a quantization table and a truncation position of the JPEG by combining a JPEG compression method from the perspective of evolutionary computation, a strategy that the truncation position is fixed when the quantization table is optimized, a strategy that the quantization table is unchanged when the truncation position is optimized, an estimation method for designing fitness evaluation and a method based on rate-distortion optimization are respectively carried out, and the quantization table and the truncation position are optimized. The optimal quantization table and the truncation position can be effectively found, and the corresponding truncation position is optimized while multiple groups of quantization tables are generated in the optimization process; through joint optimization of the quantization table and the truncation position, image data can be compressed more effectively, redundant information is reduced, and therefore coding efficiency is improved.

The present invention relates to a method and device for active defense against deep fake face based on discrete wavelet transform, and to the field of image processing technology. The method comprises: using a deep fake model to edit the attributes of the original and disturbed images, calculating the mean square error loss and structural similarity loss between the two, and merging them into a total loss function after assigning different weights; obtaining gradient information based on the back propagation of the loss function, determining the optimal direction of the disturbance application, and forming an intermediate disturbance value; embedding low-frequency and high-frequency components into the corresponding components of the disturbed image through discrete wavelet transform, and then reconstructing the image through inverse discrete wavelet transform; checking whether the number of iterations has been reached, if not, introducing a JPEG noise layer to continue iteratively adjusting the disturbance; if so, outputting a face image containing invisible disturbance information. The present invention makes full use of the robustness in the wavelet domain, and improves the resistance of disturbance to JPEG compression while ensuring that the disturbance is invisible.

A computer-implemented method that provides watermark-based image reconstruction to compensate for lossy encoding schemes. The method can generate a difference image describing the data loss associated with encoding an image using a lossy encoding scheme. The difference image can be encoded as a message and embedded in the encoded image using a watermark and later extracted from the encoded image. The difference image can be added to the encoded image to reconstruct the original image. As an example, an input image encoded using a lossy JPEG compression scheme can be embedded with the lost data and later reconstructed, using the embedded data, to a fidelity level that is identical or substantially similar to the original.

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 present invention claims protection for a strong robust blind watermarking algorithm in the NSCT-DCT domain that combines Polar codes with the SP (Successive Packing) two-dimensional interleaving algorithm, belonging to the field of image processing. This algorithm successively performs Logistic encryption on the watermark, Polar code encoding, SP two-dimensional interleaving, performs NSCT transformation on the carrier image, and embeds the watermark information according to the relationship between its coefficients and determinants, enabling blind extraction of the watermark. When extracting the watermark, geometric correction of the image is performed using the Radon transform. The results show that this algorithm not only makes the image after embedding the watermark have good invisibility, but also can effectively resist attacks such as adding noise, filtering, JPEG compression, shearing, translation, rotation, mosaic, and tampering. The NC value is above 0.9, and it also has strong robustness against combined attacks, having good practical value.

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 model integration-based deep forgery detector anti-robustness evaluation method, and belongs to the technical field of computer vision and information security. In order to solve the problems of poor migration of adversarial samples and insufficient compression robustness of an existing deep forgery detector, multiple deep forgery detection models of different structures are integrated, an adversarial sample is generated by adopting a momentum iteration-common weakness attack algorithm, and meanwhile, a differentiable JPEG compression module is embedded to simulate a real compression process. According to the technical scheme, the method comprises the steps of selecting multiple counterfeit detection models to construct an evaluation sample set; generating a basic confrontation sample by using a model integration strategy; generating a compressed robust confrontation sample in combination with differentiable compression; and the system evaluates the attack effect of the confrontation sample under different compression strengths. According to the method, the mobility and the compression robustness of the adversarial sample are improved, and the security vulnerability of the deep counterfeit detection system can be comprehensively evaluated.

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 provides a dual-stream U-Net image tampering detection network system. The network adopts a U-type encoding and decoding network structure. The encoder includes an RGB stream and a Noise stream. The RGB stream is used to extract high- and low-level tampering features in an image, so as to obtain a rough positioning effect. The Noise stream is used as a supplementary stream to reveal the local noise inconsistency in the image. The two streams are combined in a lightweight hierarchical manner, so that the network of the invention can perceive tampering targets with large differences in shape and size at different scales. After encoding by the encoder, the network adopts a decoder to simultaneously fuse encoding features and jump features to obtain rich low-level tampering traces and spatial positioning information, so as to meet the demand for accurately positioning the tampering target area. The image tampering detection method using the dual-stream U-Net image tampering detection network system can simultaneously detect three types of tampering, namely, splicing, copying and moving, and removal. The method has certain robustness and can still maintain a good detection effect in response to JPEG compression attacks with different compression factors.

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 copyright protection method and system for handwritten font image synthesis, and a storage medium. The method comprises the following steps: S1, obtaining a reference font image; s2, embedding the hash key into the reference font image through a font migration network to generate a target font image; and S3, extracting the embedded Hash key from the target font image through the extraction network, comparing the Hash key with the original Hash key, and verifying the source and authenticity of the image. By adopting the technical scheme provided by the invention, the problem of robustness in the face of JPEG compression and other common attacks is solved.

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 invention provides a color image robust reversible watermarking method based on a quaternion Zernike moment, and belongs to the field of image security. The problems of low efficiency and insufficient robustness of an existing method for embedding a robust reversible watermark algorithm into a color image are solved. The method comprises the following steps: firstly, embedding a robust watermark into a quaternion Zernike moment coefficient of a color image; then, according to the watermark embedded with the robustness, an intermediate image is generated, and the intermediate image is close to the original color image; and converting the difference value between the intermediate image and the original color image into a bit sequence, and embedding the bit sequence into the image added with the robust reversible watermark by adopting a reversible information hiding mode to finally obtain the image added with the robust reversible watermark. When the watermark is extracted, the original color image can be recovered from the image added with the robust reversible watermark, the method has strong robustness in the aspects of salt and pepper noise, Gaussian noise, JPEG compression and image blurring, the robust reversible watermark is embedded into the color image, and the method can be applied to copyright protection of color medical images.

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 present invention discloses a detection method for JPEG compression domain enhancement, including: dividing the pixel matrix of the image to be inspected into blocks, selecting available blocks suitable for enhanced detection; performing discrete cosine transform on each available block to obtain a corresponding coefficient matrix; obtaining a global likelihood function value according to each enhancement parameter candidate value and the coefficient matrix; arranging the global likelihood function value corresponding to each enhancement parameter candidate value into a vector, and performing enhanced detection as an enhancement detection feature vector. The present invention also discloses a detection device, storage medium and computing equipment for compression domain enhancement. The present invention divides the pixel matrix of the image to be inspected into blocks, designs a suitable likelihood function, uses the global likelihood function values ​​corresponding to a series of enhancement parameter candidate values ​​as enhancement detection feature vectors, and determines whether the image to be inspected has been enhanced in the JPEG compression domain, thereby obtaining accurate detection results.

The invention provides a KVM (Keyboard Video Mouse) video compression method and system. The method comprises the following steps: S1, segmenting an I frame and all P frames in an image group into pixel blocks and outputting the pixel blocks frame by frame; s2, judging the type of the current frame; if the frame is the I frame, executing the step S3; if the frame is a common P frame, executing the step S4; if the frame is the complete P frame, executing the step S6; s3, each pixel block of the I frame is sent to a JPEG compression encoder for compression encoding; the JPEG compression encoder outputs the compressed data to a data packer; then executing the step S7; step S4, performing a to-be-coded region CR, and then executing step S5; s5, sending a to-be-coded region CR corresponding to the current frame to a JPEG compression encoder; the JPEG compression encoder outputs the compressed data to a data packer; then executing the step S7; and step S6, the block comparison module directly sends a no-data frame identifier to the data packager. According to the method, the definition of the KVM video can be ensured, and a relatively high compression ratio can be achieved.

A computer-implemented method that provides watermark-based image reconstruction to compensate for a lossy coding scheme. The method is capable of generating a difference image that describes the data loss associated with encoding an image using a lossy coding scheme. The difference image can be encoded as a message and embedded in the encoded image using a watermark, and later extracted from the encoded image. The difference image can be added to the encoded image to reconstruct the original image. As an example, an input image encoded using a lossy JPEG compression scheme can be embedded with the lost data and later reconstructed to the same or substantially similar fidelity level as the original using the embedded data.

The invention provides a JPEG (Joint Photographic Experts Group) image restoration method based on DCT (Discrete Cosine Transform) and a Mama network, which comprises the following steps of: constructing a DCT (Discrete Cosine Transform) network architecture, combining the long sequence modeling advantage of Mama with the characteristic that high-frequency components are abandoned after JPEG compression, and introducing DCT into the network so as to establish a causal relationship sequence conforming to Mama autoregression scanning characteristics; the rationality of Mama for image restoration is improved, and a scale adaptive normalization method is proposed for discrete cosine transform frequency distribution differences caused by different image sizes to ensure consistent restoration of images of different sizes. According to the method, the scanning sequence conforming to the Mama autoregression characteristic can be established, and pictures of different sizes can be flexibly processed, so that the fidelity of JPEG image restoration can be improved.

The invention discloses a JPEG (Joint Photographic Experts Group) embedding optimization method based on channel overflow processing, which relates to the field of image processing, and comprises the following steps of: S1, carrying out scaling overflow processing on a cover through a scale formula to obtain a robust cover, S2, calculating a distortion value of the robust cover by utilizing a J-UNIWARD distortion function and calculating asymmetric distortion, obtaining one-time modification cost through the asymmetric distortion, and carrying out one-time modification on the one-time modification cost through the asymmetric distortion. Modifying according to the primary modification cost; s3, performing data compression and information hiding by adopting STCs coding to obtain a temporary image; and S4, performing JPEG compression on the temporary image and extracting an error rate according to compression information, if the error rate meets a threshold value, generating a final image, and if the error rate does not meet the threshold value, adjusting the error correction capability and repeating S3 and S4 until the final image is generated. The method has good robustness on JPEG pictures uploaded and downloaded by a public social platform, and solves the problem of picture distortion to a certain extent.

This invention discloses a digital image watermark embedding and extraction method, device, and storage medium. The method combines histogram shifting with neural network embedding. After the DCT transform, histogram shifting is performed to minimize noise in the embedded data stream under JPEG compression. A neural network compensation algorithm is then used to minimize the impact of the data stream on the host image. This technical solution addresses the problem of poor performance of existing neural network digital watermarking techniques against JPEG compression attacks.