92 results about "Image forgery" patented technology

The invention provides a multi-image forgery detection method and system based on a cross-modal vision large language model, and relates to the technical field of image processing and computer vision, and the method comprises the steps: constructing a data set, and carrying out the preprocessing of the data set; according to the preprocessed data, respectively extracting visual features and language features through a pre-trained visual Transform and a language model so as to obtain cross-modal features; according to the cross-modal features, the visual features and the language features are clustered, cross-modal similarity is calculated, a matching relation is established, fusion is carried out, and fused multi-modal features are obtained; and according to the fused multi-modal features, performing adversarial training through a generator and a discriminator to generate an adversarial network. According to the method, high-precision detection and effective detection of multiple image counterfeiting types such as positioning splicing, copying and pasting, AIGC generation and the like are realized.

The invention discloses an image counterfeiting multi-type detection method and system based on a visual large language model, and belongs to the technical field of image processing and computer vision. The method comprises the following steps of: preprocessing a data set containing normal, copy and paste forged and spliced forged images, extracting visual and language features by utilizing a pre-training model, and realizing feature fusion by adopting a multi-modal alignment technology. Whether an image is forged or not is judged through a classification task, a forged area is positioned through a segmentation task, and meanwhile a forged text description is output through a text generation module. The loss function is optimized in combination with classification, segmentation, comparison and text cross entropy loss. And adopting an end-to-end training and course learning strategy training model, and outputting a forgery probability, a regional mask and text description during reasoning. According to the method, multiple counterfeit types can be processed at the same time, high-precision positioning is achieved, multi-modal information is effectively fused, and detailed counterfeit text description can be provided.

The invention discloses an incremental counterfeit face image identification method based on cross-domain feature alignment, which belongs to the field of deep counterfeit detection, and comprises the following steps of: constructing a face image counterfeit detection model, extracting a network by taking Xception as a main feature when the model is constructed, a task adaptive weight correction module, a class awareness supervision comparison learning module, a double knowledge distillation module and a classification discrimination module are synchronously introduced; defining a task sequence, and constructing a task sample set and a task test set for subsequent model training and testing; a domain chain progressive training mechanism is adopted to train the face image forgery detection model, and the training process is composed of a basic stage and a plurality of incremental task stages; after training of each task is finished, testing is carried out; and obtaining a current to-be-identified face image, and inputting the final face image counterfeiting detection model to obtain an identification result. According to the method, the accuracy and generalization of face image counterfeiting detection can be effectively improved.

The invention discloses an image forgery positioning method based on double-flow multi-scale feature fusion, and belongs to the technical field of image forgery positioning, and the method comprises the steps: respectively extracting multi-scale semantic features and noise features of a normalized RGB image through a double-branch structure; stacking the same-scale features to generate preliminary fusion features; multi-scale enhancement operation is executed, and enhancement features are generated by combining global channel weighting and local space enhancement; performing up-sampling splicing on the multi-scale enhanced features, encoding space information in horizontal and vertical directions through a coordinate attention fusion module, generating direction attention weights, and performing weighted fusion on the features; and generating a prediction mask based on the weighted fusion feature, and combining a Dice loss function and a Focal loss function to optimize the model. According to the method, semantic and noise information is comprehensively utilized, the multi-scale feature expression and spatial positioning capability is enhanced, and the counterfeit positioning precision and robustness in a complex scene are improved.

The invention relates to the technical field of digital image identification, and discloses a digital image identification method and system based on artificial intelligence, and the method comprises the steps: constructing a three-layer fusion neural network architecture; analyzing the input image to generate a physical feature vector set; processing an input image to generate a semantic feature vector and a relational graph; on the basis of the physical feature vectors and the semantic feature vectors, physical semantic joint distribution is constructed by utilizing a variational reasoning engine layer; a physical semantic cross attention mechanism is realized; analyzing the deviation degree between the physical semantic joint distribution and pre-established natural image standard distribution; generating an identification result and an interpretability analysis report of the input image based on the deviation degree; according to the method, the normal form transformation from finding forgery traces to verifying naturalness is realized, a brand new theoretical basis and a technical path are provided for the field of digital image identification, and increasingly complicated image forgery challenges can be dealt with.

The invention discloses an image forgery detection method, system and device based on pixel difference perception, and belongs to the technical field of computer vision and artificial intelligence. The method comprises the following steps: S1, acquiring a real image and a forged image, and preprocessing and labeling the real image and the forged image; s2, processing the preprocessed image by using a position exhibition module; s3, performing down-sampling on the image obtained in the step S2, and extracting low-frequency information of the original image; s4, adjusting the size of the original image to be consistent with that of the down-sampled image; s5, performing subtraction on the image obtained by processing in the step S4 and the image sampled in the step S3, and calculating a difference value of adjacent pixels to form a difference value image; s6, performing up-sampling on the difference image, and extracting features; and S7, classifying the extracted features by using a lightweight deep learning network, and outputting a detection result, the method can accurately capture fine counterfeit traces in the image generation process, and significantly improves the accuracy and robustness of image counterfeit detection.

The invention discloses an image forgery detection method and system based on a large model, a terminal and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected image; obtaining an authenticity category judgment result corresponding to the to-be-detected image, an explanation text for the authenticity category judgment result and forged area indication information corresponding to the to-be-detected image through a trained multi-modal detection large model; according to the to-be-detected image and the forged area indication information, determining a pixel-level forged area positioning mask corresponding to the to-be-detected image through a trained pixel-level segmentation model; and generating a counterfeit detection result corresponding to the to-be-detected image according to the authenticity category judgment result, the explanation text and the pixel-level counterfeit region positioning mask. Thus, the generated counterfeit detection result has interpretability, pixel-level accurate indication for the counterfeit area in the image can be realized, and the image counterfeit detection effect can be improved.

The invention discloses a generative image forgery detection method based on attention guidance and incremental learning, and the method comprises the steps: constructing an end-to-end detection model which comprises a multi-modal feature coding module, an A-DTG module, an incremental learning module and a classification module; the A-DTG module generates a domain label by using a self-attention mechanism and a multi-modal attention fusion technology; the incremental learning module is combined with an online incremental learning algorithm, knowledge distillation and a transfer learning technology to realize real-time updating of the model; and optimizing model training through a classification loss function, a distillation loss function and a total loss function. The method effectively solves the problems that in the prior art, the detection capacity of a novel forgery technology is insufficient, multi-modal data processing is weak, and a model cannot be updated in real time, and can remarkably improve the accuracy, generalization and timeliness of image forgery detection in the scenes of news media, judicial evidence obtaining, social media and the like.

The invention relates to the field of forged image detection, and provides a forged image detection method and device, a medium and a product, and the method comprises the steps: dividing an input image into a plurality of local regions, and constructing a region dependence graph of the local regions; extracting local style features from the region dependence graph by using a local network model; extracting global style features of the input image by using the global network model; performing attention fusion on the local style features and the global style features by using an attention model to obtain a fusion vector; performing image forgery detection on the fusion vector by using a classifier; wherein the local network model, the global network model, the attention model and the classifier are optimized through training. According to the invention, high identification accuracy and robustness can be maintained in detection scenes of scarce samples, local detail forgery and cross-domain forgery.

The invention relates to the technical field of image detection, in particular to an AI generation military image detection method based on self-adaptive test training, which comprises the following steps: S1, preprocessing an input image to obtain a preprocessed image; s2, extracting multi-level features of the preprocessed image by a military hierarchical sensing module, and obtaining fusion features according to a cross-layer feature fusion mechanism; s3, weighting fusion features by an attention mechanism to obtain weighted features, and obtaining an image forgery probability according to an MLP classifier; s4, in a training stage, combining the loss of the main classification task and the loss of the self-supervised auxiliary task to analyze the loss so as to update model parameters to obtain a training model, and adopting a training strategy during self-adaptive testing to obtain an unknown forged model; and S5, inputting a military image to be detected into the unknown counterfeit model, and outputting an authenticity judgment result. The method can significantly improve the detection accuracy and generalization ability of AI generated military images, is suitable for multiple practical application scenes such as media information security, and has high practical value and wide application prospects.

The invention provides an image forgery detection method and system based on a multi-modal large model in the technical field of image forgery detection, and the method comprises the steps: S1, obtaining a large number of historical forgery images, generating the text description of each historical forgery image, setting a forgery area mask and a cue word template of each historical forgery image based on each text description, and constructing a data set based on each historical forgery image, the forgery area mask and the cue word template; s2, creating an image forgery detection model based on an interpretable forgery detection module guided by a domain label and a multi-mode forgery positioning module; s3, training the image forgery detection model through the data set, performing fine adjustment on the trained image forgery detection model, and deploying the fine-adjusted image forgery detection model; and S4, carrying out image forgery detection through the deployed image forgery detection model. The method has the advantages that the accuracy and generalization ability of image forgery detection are greatly improved.

The invention belongs to the technical field of image processing, and discloses a western blot image generation forgery detection method and system, a terminal and a storage medium, and the method comprises the steps: obtaining a to-be-detected image; wherein the to-be-detected image comprises a face image and a western blot image; performing feature extraction on the to-be-detected image based on a counterfeit detection model to obtain visual features; performing generation discrimination on the visual features to obtain a real discrimination result or a counterfeit discrimination result of the to-be-detected image; source discrimination is carried out on the visual features corresponding to the counterfeit discrimination result, and a discrimination result that the to-be-detected image comes from the face image or the western blot image is obtained; and outputting the western blot image to generate a forgery detection result. According to the method, the forgery recognition precision of the western blot image is improved, and the generalization ability of image forgery detection is enhanced.

The invention provides a double-flow fine-grained forgery detection method based on phase features, and relates to the field of image forgery detection, and the method comprises the steps: S1, obtaining a to-be-detected depth forgery data set, carrying out the image extraction of the to-be-detected depth forgery data set, and obtaining an original face image; s2, constructing a phase artifact mining module, and inputting the original face image into the phase artifact mining module for phase feature extraction to obtain a phase feature map; and S3, constructing a double-flow counterfeit detection network, inputting the original face image and the phase feature map into the double-flow counterfeit detection network, and judging whether the original face image is a counterfeit image or not. According to the invention, through a double-backbone network structure of the double-flow counterfeit detection network, feature extraction and fusion are carried out on the original face image and the phase feature map, counterfeit traces concerned by the two faces and the phase feature map are reserved, the ability of capturing common counterfeit features among different modes is enhanced, and the accuracy of counterfeit detection is further improved.

The invention relates to an image forgery positioning detection method based on collaborative difference optimization and multi-modal perception, and belongs to the technical field of computer vision and multimedia evidence obtaining. The method comprises the following steps: obtaining an RGB image and carrying out random data enhancement; extracting a noise map by using a noise extractor; the RGB image and the noise graph generate multi-scale features through a pre-trained cross-modal information encoder; the multi-scale features are input into an exception encoder, the exception encoder comprises a multi-layer perceptron, a linear fusion block and a linear prediction block, multi-layer mapping features are obtained through processing of the multi-layer perceptron, and an initial prediction map is output; inputting the original RGB image into a ViT model to extract global features, and inputting the global features and the multilayer mapping features into a cross-scale feature enhancement module for interactive optimization to obtain a refined positioning map; fusing the initial prediction map and the refined positioning map to obtain a final prediction map; and iterative optimization is carried out through the loss function. According to the invention, the detection sensitivity of tampered areas with different sizes can be improved.

The invention discloses an image forgery detection method and device based on a CLIP model. The method comprises the steps that firstly, a multi-mode tampering data set containing multiple forgery types and corresponding text descriptions is constructed; the CLIP model is finely adjusted through LoRA by using the data set, so that the CLIP model learns pixel-level counterfeit features, and the characterization capability of the difference between a tampered image and a real image is enhanced; the method comprises the following steps of: decomposing low-frequency and high-frequency components of an input image by discrete cosine transform; respectively fusing the fine-tuned characteristic patterns of the CLIP visual encoder with low-frequency characteristics, and outputting a multi-category forged region positioning result through a decoder; meanwhile, the pixel-level splicing trace prediction image is fused with the high-frequency features, and a pixel-level splicing trace prediction image is output through a decoder. According to the method, the generalization ability of the model in a real scene is effectively improved, identification of specific counterfeiting means is realized, edge traces of splicing tampering are accurately revealed, and more comprehensive technical support is provided for image tampering evidence obtaining.

The invention relates to the technical field of image detection, and provides an image forgery detection method based on a multi-modal large language model, which comprises the following steps: inputting a to-be-detected image into the multi-modal large language model; the multi-modal large language model outputs an authenticity identification result of the to-be-detected image; wherein the multi-modal large language model has three parallel expert branches of physical consistency, semantic consistency and underlying structure clue analysis. According to the method, semantic information in the image is perceived and the context logic and the physical logic of the image are understood by fully utilizing the powerful priori knowledge, the deep semantic understanding capability and the chained reasoning capability of the multi-modal large language model, and meanwhile, complex reasoning and explanation can be performed on a forged image through a natural language.

The invention discloses a student model training method, an image forgery detection method and computer equipment, and belongs to the field of image detection. The image forgery detection method comprises the steps of determining a to-be-detected target image; calling a trained student model based on the target image, and generating a detection result; wherein the student model is obtained by training fusion counterfeit features, training images and intermediate features; the training image is provided by more than two domains; the intermediate feature is generated by calling a corresponding teacher model based on the training image; the fusion counterfeit feature is generated by calling a teaching-assistant model based on all the intermediate features; the fusion forgery features refer to features corresponding to common information in all the intermediate features. According to the method, the accuracy of identifying whether the image is a forged image or not can be improved.

The invention discloses a method for detecting image forgery and computing equipment. The method for detecting image counterfeiting comprises the following steps: extracting text information in a to-be-detected image to obtain a first text; generating image description information of the to-be-detected image to obtain a second text; and based on the first text and the second text, determining whether a logic anomaly exists in the to-be-detected image.

Provided are an apparatus and method for verifying forgery of a medical image using a hash value based on meta information of the medical image.SOLUTION: The forgery verification apparatus 400 includes a memory configured to store patient-specific DICOM files and a processor functionally connected to the memory, and the processor is configured to, when issuance of a DICOM file is requested, search for the requested DICOM file among the patient-specific DICOM files, generate an issuance number associated with the issuance, extract a part of meta information of the DICOM file, generate a first hash value using the part of the meta information and the issuance number, and store the first hash value in the memory in association with the issuance number. While providing a copy of a DICOM file to an external storage device, a first hash value is inserted into a designated tag area of the copy as forgery verification information.SELECTED DRAWING: Figure 1

The invention provides an image forgery detection method and system based on asymmetric anchoring of a multi-modal large model, and belongs to the technical field of image detection processing. Truth value anchor point features and to-be-optimized intermediate features of an input image are extracted; an asymmetric strategy is executed based on the authenticity category of the image, and anchoring alignment loss is introduced only for the real image to anchor real feature distribution; extracting global semantic features, and mapping the global semantic features into the authenticity category probability of the image through a classification projection head; and generating a space alignment error graph by using the deviation between the intermediate feature to be optimized and the true value anchor point feature, injecting the space alignment error graph into a positioning decoder, and generating a forged region mask by combining the output feature of the positioning encoder. According to the method, a true value anchor point is redefined, and an asymmetric anchoring mechanism is utilized to force real image features to return to real world priori, so that the problem of characterization drift is solved; and generating a spatial error graph by using the feature alignment deviation, thereby realizing stable learning of forged clues and realizing positioning of an accurate tampering region.

The application relates to an image forgery positioning device and method based on multi-scale attention, and belongs to the technical field of image processing, solving the problems that the edge of a positioning area is not clear enough and the positioning result is not accurate enough in the prior art. The application generates synthetic forged images of various types and modes through a traditional forged image synthesis module, enhancing the generalization of the device to various types of forgery. The forged area edge feature extraction module extracts and strengthens the part related to the forged area edge in the forged area feature, fully utilizes the forged traces brought by the edge operation, and has high edge definition. The multi-scale spatial attention module gradually fuses spatial features of different scales, provides coarse-grained forged area prediction from small-scale features, combines large-scale features and edge features for fine-grained forged area prediction, and realizes more precise forged area positioning. The device training is easy to control and sufficient.

The invention discloses a layered fine-grained image forgery detection method and a layered fine-grained image forgery detection system. And the authenticity of the input image is preliminarily judged through the authenticity judgment module. Constructing a multi-branch pixel-level counterfeit positioning network, and obtaining multi-scale feature representation from low resolution to high resolution through a feature extraction backbone network; a high-resolution forged mask is obtained through pixel-level positioning network prediction; and the decision network constructs a coarse-to-fine reasoning path according to the multi-level label system, sequentially completes forgery property judgment, forgery type judgment, method-level traceability and platform-level traceability, and outputs a conditional probability of a corresponding-level label as a traceability result. The method has a unified multi-level label system, the systematicness and the accuracy of detection are remarkably improved, the source and the position of counterfeiting are visually presented through mask positioning and hierarchical classification, and the interpretability is high.

The invention discloses an image anti-counterfeiting method and device, electronic equipment and a storage medium. The method comprises the following steps: shooting a first image to obtain a plurality of second images; and comparing the plurality of second images with the plurality of third images, and performing anti-counterfeiting authentication according to a comparison result. According to the method, the anti-counterfeiting of the image can be realized through the first image, and the difficulty of image counterfeiting is also increased.

The invention discloses an image forgery detection method based on a camera signal shaping effect, and belongs to the technical field of image forgery generation security, and the method comprises the steps: S1, obtaining original single-channel Bayer format data, dividing the original single-channel Bayer format data into space block units, generating an interpolation index template, matching a specific algorithm index for each image block according to a preset algorithm library and an interpolation index template; and S2, executing verification and detection of an interpolation index template generation mode at a detection end, and authenticating the authenticity of the image and accurately positioning a tampered area by comparing the consistency of physical signal characteristics of the image and encryption verification logic. According to the invention, the problem that high-precision positioning and identification of image tampering behaviors cannot be realized in the prior art is solved. On the premise of not changing the physical hardware of the camera, the verification information is embedded into the texture structure of the image by utilizing the variability of the demosaicing algorithm, so that the problems of image authenticity traceability and local tampering positioning are solved from the source.

1. Name of the Design Product: Handheld Microdot Detector. 2. Use of the Design Product: The design product is used for image forgery identification, microdot code reading and detection in patterns. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Perspective View 1.

This invention discloses a method, apparatus, device, and medium for multi-class image forgery detection, relating to the field of image processing technology. The method can identify real faces, forged faces, real anime faces, forged anime faces, real sketch faces, and forged sketch faces. A multi-branch network, by integrating the characteristics of pre-trained models such as EfficientNet, ResNet, and DenseNet, combined with a feature fusion attention layer, significantly improves the classification ability for complex image categories. To construct a high-quality forged sketch dataset, a CycleGAN-based forged image generation framework was implemented. Adversarial loss, cycle consistency loss, and perceptual loss were used to optimize the generation effect, enhancing the diversity and robustness of the dataset. Experimental results show that the proposed model exhibits excellent performance in multi-class classification tasks, with an average accuracy and F1 score of 98.12%, demonstrating excellent generalization ability in both real and forged image classification.

The one or more embodiments of the specification disclose a method, device and equipment for image forgery detection. The method comprises: performing feature extraction on each pixel point in a to-be-detected image to obtain image features corresponding to each pixel point in the to-be-detected image; inputting the image features corresponding to each pixel point in the to-be-detected image into a forgery detection model to perform forgery feature similarity matching between the image features corresponding to any two pixel points in the to-be-detected image, and obtain a forgery feature similarity value between any two pixel points in the to-be-detected image; determining, according to the forgery feature similarity value, a forgery probability map of the to-be-detected image corresponding to each pixel point in the to-be-detected image; and in a case where a proportion of pixel points with a forgery probability value exceeding a preset probability in the forgery probability map exceeds a first preset proportion, determining a forgery region in the to-be-detected image according to the proportion of pixel points with the forgery probability value exceeding the preset probability in the forgery probability map.

The application provides a picture forgery detection method, comprising: extracting frequency domain features and spatial domain features of a to-be-detected picture and a plurality of derived pictures, wherein each derived picture is generated after a first operation is performed on the to-be-detected picture, and the to-be-detected picture is a picture of a preset size; determining intermediate feature vectors of the to-be-detected picture and the derived pictures according to the frequency domain features and the spatial domain features, respectively; inputting the intermediate features into a true-false discriminator and a model tracer in parallel, wherein the true-false discriminator is a binary classifier, and the model tracer is a multi-class classifier; determining a fake or real detection result according to the intermediate feature vectors by using the true-false discriminator, and generating a multi-class probability distribution according to the intermediate feature vectors by using the model tracer, wherein the multi-class probability distribution refers to a probability distribution of a picture forged by a forgery model; outputting information indicating whether the to-be-detected picture is real or fake based on the detection result of the true-false discriminator and the probability distribution generated by the model tracer, and outputting the probability distribution at least in the case that the picture is fake. Corresponding systems, program products, and the like are also provided.

The invention discloses an image forgery positioning system based on a bimodal vision Mangbar network, which relates to the technical field of image forgery positioning and comprises a model construction unit and a forgery positioning unit. The model construction unit is used for preprocessing the to-be-detected image and acquiring an RGB modal image and a corresponding brightness gradient modal image; respectively carrying out feature extraction to obtain RGB features and corresponding brightness gradient features, carrying out fusion to obtain fusion features, and carrying out decoding through a decoder to obtain prediction maps of all stages; and the forgery positioning unit is used for receiving the prediction map of each stage, obtaining a final prediction map by adopting a voting mechanism, setting a confidence coefficient threshold value, judging an area of which the pixel value is greater than the confidence coefficient threshold value as a forgery area, superposing the final prediction map subjected to morphological operation processing with an original input image, and outputting a visual positioning result. According to the method, RGB and brightness gradient bimodal features are combined, and the positioning precision of weak-trace and high-fusion-degree forged areas is greatly improved.

The invention discloses a forgery positioning method based on attention enhancement and adaptive frequency selection, and belongs to the technical field of image forgery detection. The invention aims to solve the problem of poor positioning precision and robustness caused by inaccurate feature capture of a forged area and insufficient utilization of multi-scale and frequency information in the existing forged positioning technology. The scheme comprises the following steps: extracting multi-scale spatial features of an input image through an encoder; channel and space attention weighting is carried out on the multi-scale features by using an attention enhancement module so as to enhance and counterfeit related features; carrying out discrete cosine transform and adaptive weight learning on the enhanced features by adopting an adaptive frequency selection module, screening effective frequency components and carrying out cross-domain fusion; and finally, generating a positioning mask of the forged region based on the fusion features. The method can effectively improve the positioning accuracy of the forged area in the image and the robustness in a complex scene, and is suitable for digital media evidence obtaining and content security auditing.