162 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 forgery detection method and system based on multi-expert model decision, and belongs to the technical field of image forgery detection. The method comprises the following steps: acquiring a multi-field normal image, generating forged images based on different forging types and forging degrees, grouping the forged images, pre-training a pre-screening model and a multi-expert model capable of predicting the forging probability by using the normal image and each group of forged images, and focusing on different features or forging traces of the image by each expert model; training an image forgery detection network comprising an image coding module, a gating calculation module, a pre-screening model and a multi-expert model by using the normal image and the forgery image; during training, parameters of the pre-screening model and the multi-expert model are frozen; and identifying whether the input image is forged based on the trained image forging detection network. According to the method, multi-dimensional detection of the forged image can be realized, a result is comprehensively output through a weighted fusion decision mechanism, the advantages of different models are fully utilized, and the detection precision and efficiency of the forged image are improved.

The invention discloses a multi-feature fusion image forgery detection method based on a high-resolution network. The method comprises the following steps: (1) training a binary classifier by using a real image and a manipulated image, and storing a model; (2) inputting the image into a pre-trained classifier; calculating the gradient of the image; (3) inputting the generated gradient map and the original image into a feature extraction module to extract gradient features and color space information; the features of the two branches are fused through a convolutional network; (4) inputting the original image into a frequency domain feature extraction block, and extracting high-frequency features of the image; (5) further fusing the fused gradient features and color space features into frequency domain features; a high-resolution network is used as a backbone network in a feature extraction and fusion stage; and (6) the fused features are sent to a discriminator, and whether the input image is real or forged is judged. The method has higher recognition accuracy and robustness, and can be well generalized to unseen counterfeiting methods.

The invention discloses a multi-category image counterfeiting detection method, device and equipment and a medium, and relates to the technical field of image processing, and the method can realize the recognition of a real face, a counterfeited face, a real cartoon face, a counterfeited cartoon face, a real sketch face and a counterfeited sketch face. According to the multi-branch network, through integrating characteristics of pre-training models such as the EfficientNet, the ResNet and the DenseNet, and combining a feature fusion attention layer, the classification capability of complex class images is remarkably improved. In order to construct a high-quality counterfeited sketch data set and realize a counterfeited image generation framework based on CycleGAN, the diversity and robustness of the data set are enhanced by optimizing the generation effect by using adversarial loss, cyclic consistency loss, perception loss and the like. Experimental results show that the provided model shows excellent performance in a multi-category classification task, the average accuracy and the F1 score respectively reach 98.12%, and the model shows excellent generalization ability in classification of real and forged images.

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 relates to the technical field of image forgery detection, in particular to an image forgery detection method based on self-supervised contrast learning. According to the technical scheme, the method comprises the steps that an image to be detected is input into a trained image forgery detection model, and an image forgery detection result of the image to be detected is obtained; the image forgery detection model comprises a feature extractor which is pre-trained through self-supervised contrast learning and is used for extracting an image feature I of an image to be detected; the classifier is used for classifying the to-be-detected image into a real image or a forged image based on the image features I extracted by the feature extractor; the self-supervised contrast learning pre-training takes a real image set as a positive sample, and takes the real image set and a forged image set as negative samples. According to the method, feature distribution of a real image and a forged image is mined through self-supervised comparative learning, and the problem of generalization of a detection model is solved.

The invention relates to an image forgery active defense method based on multi-watermark fusion and cross-domain learning, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring a to-be-processed image; an image to be processed is subjected to invisible watermark embedding and visible watermark embedding through a watermark encoder, and an image embedded with an invisible watermark and an image embedded with a visible watermark are obtained respectively; processing the image embedded with the invisible watermark through a noise layer to obtain a noise image; the image embedded with the visible watermark is processed through a noise layer, and an obvious false warning mark is generated at the position of the image embedded with the random noise through visible watermark joint optimization; the noise image is traced and detected through a watermark decoder, and the authenticity of the image is judged; and carrying out loss function supervision training. According to the method, whether the image is deeply counterfeited or not can be accurately judged, and the authenticity of an image source can be verified.

The invention discloses an image forgery detecting and positioning method based on noise auxiliary prompt learning, which introduces a contrast language image pre-training model (CLIP) and prompt learning into the field of image forgery detecting and positioning, and comprises the following steps of: 1, respectively processing an input image and noise thereof by using an image encoder of the CLIP and a visual transformer to obtain double-branch features, the forgery perception capability of the network is enhanced through multi-domain fusion and a memory mechanism; 2, creating a pair of learnable prompts as negative and positive samples to replace discrete prompts, and finely adjusting the prompts according to the features and categories of each image; and 3, completing forgery detection by calculating similarity between prompt and image features, and integrating image and text features through feature linear modulation to obtain a forgery positioning result. According to the invention, the accuracy and generalization of image forgery detection and positioning can be improved, and the false alarm problem can be reduced, so that the performance of image forgery detection and positioning can be improved.

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.

Systems and / or techniques for facilitating image forgery detection via pixel-metadata consistency analysis are provided. In various embodiments, a system can receive an electronic image from a client device. In various cases, the system can obtain a pixel vector and / or an image metadata vector that correspond to the electronic image. In various aspects, the system can determine whether the electronic image is authentic or forged, based on analyzing the pixel vector and the image metadata vector via at least one machine learning model.

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 provides an image detection method and device, equipment and a storage medium, and relates to the technical field of computers, in particular to the technical fields of image recognition, forgery recognition, image forgery, artificial intelligence and the like. According to the specific implementation scheme, first sampling is performed based on an input image to obtain a first sampling result set; performing second sampling based on the last sampling result in the first sampling result set to obtain a second sampling result set; and obtaining a detection result of the input image based on the difference between the first sampling result set and the second sampling result set. According to the method and the device, a more accurate detection result of the input image can be obtained by comparing the differences of the sampling result sets in different sampling modes.

The invention discloses a deep counterfeit video detection method fusing ViT and spatial features, belongs to the technical field of video identification, and is used for detecting a deep counterfeit video. The method comprises the following steps: constructing a neural network fusing ViT and spatial features; training a deep counterfeit video detection network; acquiring face data information in the deep forged video; and inputting the processed video information into the trained deep forged video detection network, and outputting whether the video information belongs to a forged video or not. According to the method, the advantages of the convolutional neural network in image counterfeiting detail extraction in video counterfeiting detection are fully utilized, thoughts of orthogonal convolution, an attention mechanism, residual connection and the like are combined, the accuracy of deep counterfeiting video detection is improved while the complexity of the model is maintained, and the detection efficiency of the deep counterfeiting video is improved. And moreover, the method has relatively stable detection performance in videos generated by various counterfeiting technologies.

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 relates to the field of network security technologies and the like, discloses a multi-mode deep counterfeiting detection model for time counterfeiting positioning, and aims to solve the problems that in an existing detection method, fine scale counterfeiting features are not expressed sufficiently and image counterfeiting artifacts cannot be effectively captured. Comprising a visual feature extraction module and an audio feature extraction module; the self-adaptive multi-modal feature fusion module is used for fusing visual feature representation and audio feature representation in a feature channel dimension in a mode of constructing an attention map; the frame classification module is used for processing the visual feature representation and the audio feature representation to obtain a visual frame-level prediction label and an audio frame-level prediction label; and the boundary positioning module is used for processing the visual feature representation, the audio feature representation, the multi-modal fusion feature, the visual frame level prediction label and the audio frame level prediction label to obtain final forged boundary prediction.

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 discloses an image forgery detecting and positioning method based on lottery hypotheses and a mask auto-encoder, which comprises the following steps of: 1, pre-training an image by using the mask auto-encoder, and extracting priori knowledge of a natural image; 2, identifying parameters sensitive to a forgery detection task in a pre-training stage based on the lottery hypothesis to obtain a gradient mask; and 3, constructing a multi-source counterfeit sensing network, realizing sparse fine tuning optimization based on gradient masks, and training the network so that the network can output accurate detection results and positioning results. According to the method, the feature extraction capability of the mask auto-encoder and the parameter screening strategy of the lottery hypothesis are combined, and the accuracy and generalization of counterfeit detection and positioning are remarkably improved through sparse fine tuning and multi-source feature fusion.

Systems and methods for detection and localization of image and document forgery. The method can include the step of receiving a dataset having a plurality of authentic images and a plurality of manipulated images. The method can also include the step of benchmarking a plurality of image forgery algorithms using the dataset. The method can further include the step of generating a plurality of receiver operating characteristic (ROC) curves for each of the plurality of image forgery algorithms. The method also includes the step of calculating a plurality of area under curve metrics for each of the plurality of ROC curves. The method further includes the step of training a neural network for image forgery based on the plurality of area under curve metrics.

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 an image tampering positioning and detecting method and device based on a large model, and a medium. The method comprises the following steps: constructing an image tampering positioning and detecting network model based on a preset high-pass filter, a preset feature fusion encoder, a preset large model network and a preset full-connection network; training the image tampering positioning and detection network model by using the training data set to obtain a trained image tampering positioning and detection network model; inputting a to-be-detected image into the trained image tampering positioning and detection network model, obtaining positioning output, and obtaining a tampering region positioning result; and inputting a to-be-detected image into the trained image tampering positioning and detection network model, obtaining detection output, and obtaining a detection result used for reflecting whether the to-be-detected image is tampered or not. The image tampering localization and detection network model obtained through training can improve the tampering area localization and tampering image detection performance of the tampering image, and has excellent generalization and robustness.

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.