513 results about "Forgery detection" 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 provides a face forgery detection method and system based on reconstruction learning and a hybrid expert mode. The method comprises the following steps: extracting a face frame image from a face video; performing data processing on the face frame image to obtain a two-dimensional face image; swin Transform is used as an encoder, a hybrid expert model is introduced at the same time, and spatial feature and long-distance dependence feature extraction is carried out on the two-dimensional face image; enhancing different types of features in the two-dimensional face image by using a hybrid expert model to obtain a high-level semantic feature map; generating a face reconstruction image by using an attention-guided decoding module; and aggregating the face reconstruction image and the corresponding two-dimensional face image by using a feature aggregation module based on reconstruction guidance, and exploring a possible counterfeit region to obtain a final feature map for face counterfeit detection. According to the method, the detection capability of unknown counterfeit types is better, and the accuracy, robustness and generalization of face counterfeit detection can be improved.

The invention discloses a deep counterfeiting detection model training method, a deep counterfeiting detection method and a deep counterfeiting detection system, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a training data set containing a real image and a plurality of counterfeit images, and enabling the image to be provided with a label for representing the authenticity; in the training process, the deep forgery detection model can be in contact with various types of image samples, so that wider and more complex image features and forgery modes can be learned, a forgery reason is further marked for a forgery image, and the deep forgery detection model can be helped to deeply understand essential features of forgery content in the training process. Therefore, the problem of insufficient detection capability for well-designed and high-quality counterfeited contents can be solved, and the technical effect of improving the accuracy of counterfeited detection is achieved.

The invention discloses a deep forgery detection method based on a visual language model, and relates to the field of image forensics. The deep forgery detection method based on the visual language model aims to combine multi-source information to improve the discrimination capability of the model on a real image and a generated image. The method comprises the following steps: firstly, extracting image features through an image encoder of a pre-trained CLIP model; meanwhile, a frequency domain enhanced counterfeit perception adapter is embedded in the image encoder to mine potential anomalies of counterfeit images in the image domain and the frequency domain. Secondly, a manual feature extraction module is provided, discriminative low-dimensional features are extracted from the four aspects of the edge, the texture, the frequency and the symmetry of the image, and the discriminative low-dimensional features are used as auxiliary information input in the forgery detection process, so that the robustness and the interpretability of the model are improved; meanwhile, the text cue words are converted into feature vectors through a text encoder of a pre-training CLIP model; and finally, the model predicts a forgery score by calculating the cosine similarity between the image features and the text features so as to realize the discrimination of the authenticity of the image. According to the method, the problem that the detection capability of the model on the cross-dataset is insufficient is effectively improved.

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 cross-domain face anti-counterfeiting detection method and device based on multi-modal text enhancement, and relates to the technical field of network information security, and the method comprises the steps: inputting two types of description texts into a pre-trained text encoder, and extracting text type features representing reality / deception, inputting the image into a pre-trained visual encoder to extract visual features; a trainable text prompt is added to each layer of the text encoder, a trainable visual prompt is added to each layer of the visual encoder, and the visual prompt of each layer of the visual encoder is obtained by converting the text prompt of the current layer through a full connection layer; and embedding the PFT module and the TIM module into a middle layer of each layer of a text encoder and a visual encoder to realize feature interaction fusion, obtaining cosine similarity and masks between text category features and visual features, and performing face true and false classification. Based on the PFT module and the TIM module, modal feature interaction is completed in the feature extraction process at the same time, and the cross-domain detection performance is 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 provides a deep counterfeit detection method based on self-mixing, and compared with the prior art, the method generates a mixed image containing counterfeit artifacts by using a self-mixing technology, and carries out the precise detection and recognition of an artifact region of an input image through combining a backbone network OfficientNet-b3 and a counterfeit artifact detection module. The method comprises the following steps of: firstly, performing framing processing on a real image, generating a source image and a target image by utilizing a self-mixing technology, and generating a mixed image with counterfeit artifacts by combining a mask generator with an alpha blowing model; and then, the backbone network extracts global features from the mixed image, and the fake artifact detection module reinforces local feature expression of a mixed region through an artifact space cooperation attention module, so that an artifact region is accurately positioned. And finally, fusing global and local features by using residual connection, and transmitting the fused features to a full connection layer to complete classification. According to the method, various forged image types can be effectively detected, and excellent generalization ability is shown in cross-dataset and unknown scenes.

The invention relates to a depth forgery detection method, device and equipment based on cross-modal mask modeling. The method comprises the following steps: constructing a deep forgery detection model comprising a multi-mode mask modeling module, a trans-attention fusion module and a contrast learning module; performing training optimization on the deep forgery detection model by integrating the audio and video reconstruction loss and the audio and video comparison learning loss; and inputting a to-be-detected audio and video sequence into the trained deep forgery detection model, outputting to-be-detected audio and video trans-attention coding features according to the model, carrying out audio and video matching correlation detection by inputting the to-be-detected audio and video trans-attention coding features into the classifier, and outputting a forgery detection result of the audio and video sequence. By adopting the method, the characterization of a real video can be learned by utilizing a mask modeling technology, and the characteristics of an audio-visual mode are subjected to cross fusion and comparative learning, so that the forgery detection performance is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an image detection method, device, equipment and medium based on adversarial generation. Performing global feature modeling by adopting an image feature extraction module of a self-attention mechanism, generating a forgery probability graph in combination with a forgery region recognition module, constructing a joint loss function based on a detection loss value and an adversarial loss value, and optimizing model parameters of a generator, the feature extraction module and the recognition module through the joint loss function to obtain a forgery probability graph; and finally, a counterfeit detection model for identifying counterfeit information in the image is formed. According to the method, the diversity of training data is improved through adversarial sample generation, the image feature modeling capability is enhanced through a self-attention mechanism, multi-dimensional loss optimization is realized through fusion of counterfeit region difference information, and the detection precision and robustness of the model to a counterfeit image are improved.

The invention discloses a multi-mode counterfeit video detection method based on a multi-head addition cross attention mechanism, and belongs to the technical field of video counterfeit detection. The method comprises the following steps: preprocessing a video stream, decomposing a single-frame positioning face, and extracting an audio to generate a Mel spectrogram slice; the 3D convolutional network extracts video spatio-temporal features and motion differences, and the filter bank extracts audio features in combination with the residual network; audio features are mapped to a video alignment space through asymmetric projection, the video features are subjected to bidirectional interaction with an audio input multi-head addition cross attention module after being subjected to time sequence coding, and audio dominant and video dominant features are generated and are cascaded and fused with original features; and constructing cross-modal similarity loss constraint feature distribution, and fusing feature dynamic weighting and time sequence compression to output four classification probabilities of audio-visual double true, audio-visual double pseudo, video pseudo-audio true and video pseudo-audio pseudo. The multi-mode counterfeiting recognition precision is improved, and texture abnormity and audio and video mismatch features are captured.

The invention provides a multi-mode depth forgery detection method and device, and the method comprises the steps: obtaining a video frame sequence and an audio sequence of a target source video, carrying out the feature extraction of the video frame sequence based on a preset visual feature extraction model, and obtaining a visual feature, performing feature extraction on the audio sequence based on a pre-trained audio spectrogram coding model to obtain audio features, fusing the visual features and the audio features to obtain corresponding fusion features, obtaining a preset target multi-modal deep counterfeiting detection model, performing counterfeiting identification on the fusion features based on the target multi-modal deep counterfeiting detection model, and obtaining a target multi-modal deep counterfeiting detection model. And determining an identification result of the target source video.

The invention discloses a face depth forgery detection method based on an enhanced double-branch fusion model. According to the method, a novel enhanced double-branch fusion model is adopted, so that the robustness and generalization ability of a CNN-based face depth forgery detector are improved. Firstly, RGB high-frequency noise in a face image is enhanced to extract abnormal features of the face image, and then reservation fusion is carried out. Specifically, a depth separable convolution module is used to improve model performance when image features are extracted. A selective kernel module is also used to adaptively extract more representative noise features by dynamically adjusting a convolution kernel. Besides, a multi-scale channel space attention fusion module is designed, and feature information of each part is effectively fused, so that overfitting of the model is reduced, and robustness and generalization ability of the model are enhanced. And finally, comprehensive evaluation on a plurality of reference data sets proves that the method is remarkably improved in the aspects of robustness and generalization.

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 belongs to an image processing technology, and particularly relates to a deep forgery detection method and system based on face embedded difference guidance, and the method comprises the steps: generating a forgery image according to an input real image, and calculating a difference image and a semantic difference vector of two images; learning the features of the real image and the forged image by using a first classifier, and learning the features of the real image and the difference image by using a second classifier; splicing and fusing the semantic difference vector and image features extracted from the difference image through a channel to obtain fused features, and learning the difference between the real face features and the fused features by using a third classifier; and calculating the distillation loss between the fusion feature and the image feature of the forged image, and training in combination with the loss of each classifier. According to the method, multi-level feature fusion, a three-classifier architecture and a knowledge distillation mechanism are adopted, the strong generalization ability across data sets is achieved, and the system can output a high-precision deep forgery judgment result.

The invention relates to an unsupervised face forgery detection method and system based on frequency domain supplementation, and belongs to the technical field of computer vision. The detection method comprises the following steps: inputting a face image to be detected into a reference network based on ResNet, and extracting multi-level coding features through an encoder of a frequency domain enhanced variational auto-encoder; performing frequency domain enhancement processing on the multi-level coding features, generating high-frequency enhancement features, inputting the high-frequency enhancement features to a decoder of the frequency domain enhancement variational auto-encoder, performing feature reconstruction in combination with a dynamic spectrum loss mechanism, and generating reconstructed frequency domain supplementary features; global pooling and local pooling are carried out on the multi-level coding features, global representation and local representation are generated and connected along a channel axis, and fused multi-scale features are generated; calculating an abnormal score graph based on the fused multi-scale features and the reconstructed frequency domain supplementary features; and determining an image authenticity result of the to-be-detected face image according to the pixel-level score of the abnormal score graph. According to the invention, the accuracy of face counterfeiting detection can be improved.

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 provides a face forgery detection method based on multi-domain clue reconstruction, and the method comprises the following steps: collecting face video data, and extracting face images of an Xi-th frame and an (Xi + n)-th frame; constructing a convolutional neural network model, and initializing parameters of the convolutional neural network model; generating a feature enhanced image by using the face image of the Xi frame and the face image of the Xi + n frame; potential spatial features are generated from the feature enhanced image, a reconstructed image is generated, and multi-scale features of the potential spatial features are extracted; generating fusion features by using the potential spatial features and the multi-scale features; generating a residual image by using the reconstructed image and the (Xi + n) th frame of face image, and generating a global attention feature by using the fusion feature and the residual image; and calculating a loss function, and carrying out face counterfeiting detection to judge whether a counterfeiting trace exists or not. The method provided by the invention can realize accurate detection of the forged image.

The invention discloses a face forgery detection method based on a feature enhancement Mama framework, and relates to the technical field of image processing, and the method is characterized in that the method comprises the following steps: S1, preparing a data set; s2, constructing a Mama framework based on feature enhancement; according to the framework, a local enhancement state space module is introduced on the basis of an original Mama framework, and the module comprises a global feature enhancement module and a local feature enhancement module; s3, training the model; the specific image feature extraction process is as follows: S31, feature extraction of the global feature enhancement module; s32, feature extraction of a local feature enhancement module; s33, fusion features are obtained; and S4, constructing a loss function. The technical problem to be solved by the invention is to provide a face forgery detection method based on a feature enhancement Mama framework, the framework can accurately capture global and local features of an image, a good effect is achieved in complex and changeable face forgery detection, and a forgery face image can be effectively identified.

The invention provides an adversarial attack method for deep forgery detection based on a diffusion model, and the method comprises the steps: taking a deep forgery picture as an attack sample blueprint, and taking a deep forgery detection model as an attack object; initializing attack method parameters; performing T rounds of iteration, performing adversarial disturbance addition from 0 to T-1 rounds on the deeply-forged picture through a momentum iteration type fast gradient symbol method in each iteration, and adjusting the attenuation speed of momentum through an attenuation factor; calculating whether the disturbance intensity parameter of the processed deeply-forged picture exceeds a set value or not in each round of iteration, and if the disturbance intensity parameter exceeds the set value, trimming; and after T rounds of iteration are carried out, de-noising is carried out on the obtained sample through a diffusion model to obtain a more natural antagonistic sample, and the antagonistic sample is applied to the deep forgery detection model to detect an attack effect. According to the method, the attack concealment is improved, and the purpose of an invisible interference detection model is achieved.

The invention discloses a face forgery detection algorithm based on style-guided multi-view fusion processing. The method comprises the following steps: firstly, carrying out standardized preprocessing on a face video sample, and extracting multi-scale image features based on an OfficientNet-B4 backbone network; by constructing a local texture map, an attention enhancement map and a style vector sequence, precise modeling and discrimination of a forged area are realized. The algorithm further utilizes a multi-branch sequence convolutional network to carry out time sequence modeling on fusion features, and outputs global style change representation for classification of forged and real images. The method comprehensively fuses the spatial texture, the semantic style and the time feature, has the advantages of high detection precision, strong generalization ability, good robustness and the like, and is suitable for complex and diverse depth forgery detection tasks.

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 relates to the technical field of biological recognition, and discloses an identity authentication system based on a biological recognition fingerprint technology, and the system comprises a fingerprint data collection module which is used for collecting fingerprint two-dimensional image data and three-dimensional point cloud data through a multi-mode technology; the fingerprint feature extraction module is used for extracting feature vectors from the collected two-dimensional image data and three-dimensional point cloud data; the forgery detection module is used for analyzing dynamic pressing behaviors recorded in the collection process and detecting forgery fingerprints; the data privacy and security storage module is used for encrypting and storing the fingerprint feature template and executing feature matching in an encryption domain; and the identity authentication module is used for fusing the fingerprint features and other biological features. According to the method, the identity authentication precision and robustness are improved through the multi-modal dynamic feature fusion technology, the Paillier homomorphic encryption and dynamic behavior detection technologies are combined, data privacy protection is achieved, and the fake fingerprint recognition capability and the system security are remarkably enhanced.

The invention discloses an audio and video depth forgery detection method based on quality perception and multi-scale alignment, and the method comprises the following steps: coding a synchronous audio and video sequence, and obtaining a frame-level visual feature, a facial action unit and a phoneme-level voice representation; a visual quality evaluation module is introduced to generate a spatial reliability mask, and quality weighting is carried out on the visual features; designing a global-local multi-scale cross-modal alignment mechanism, performing bidirectional cross-attention modeling on voice and face dynamic synchronization globally, and performing physiological coupling alignment on phonemes and face action units locally; and an uncertainty perception reasoning and calibration scheme is provided, adaptive temperature scaling is carried out according to quality and consistency, and uncertainty calibration is carried out by self-supervision loss. According to the method, the problems of insufficient robustness and excessive self-confidence misjudgment of an existing method in a low-quality video and high-synchronization counterfeit scene are solved, and the cross-dataset generalization capability and the actual deployment reliability are remarkably improved.

The invention discloses a deeply-forged face video frame-level positioning method and system based on weak supervised learning, and the method comprises the steps: firstly constructing a training set with a video as a unit, carrying out the data enhancement of a frame-level sample, and generating an enhanced view pair; secondly, splicing the enhanced view pair and inputting the spliced enhanced view pair into a depth forgery detection model to obtain and generate fusion enhanced frame-level features; then, intra-class contrast learning loss, time sequence consistency constraint loss and frame weight loss are constructed, and a deep forgery detection model is trained based on fusion-enhanced frame-level feature joint optimization. And finally, inputting a video to be detected into the deep counterfeiting detection model to output the frame-level confidence, judging whether the video is a forged video or not, and realizing frame-level counterfeiting positioning. According to the method, video-level detection and frame-level positioning are effectively realized, meanwhile, the influence of label noise in part of forged videos is relieved, and the generalization and robustness of the system are 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.

The invention relates to the technical field of image detection, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a video content counterfeiting detection method, device, equipment and medium, and the method comprises the steps: obtaining a to-be-detected video stream, carrying out the frame sampling and standardization processing of the to-be-detected video stream, and generating a to-be-detected video sequence; performing visual double-branch feature extraction on the to-be-detected video sequence to obtain a universal visual feature, a local counterfeit feature and an audio feature; fusing the universal visual features, the local counterfeit features and the audio features to obtain audio and video consistency features; mapping the audio and video consistency feature to a low-dimensional decoupling space and carrying out feature decoupling to obtain a target counterfeit feature; and performing expansion convolution on the target counterfeiting feature to obtain a target counterfeiting probability sequence, and determining authenticity of the to-be-detected video stream according to the target counterfeiting probability sequence. According to the invention, the content counterfeiting detection efficiency and detection accuracy can be improved.

The invention relates to the technical field of information detection, and discloses an information forgery detection method, computer equipment, a storage medium and a program product, and the method comprises the steps: obtaining to-be-detected information which comprises at least two types of modal information; determining interaction characteristics among different modal information in the to-be-detected information; on the basis of the interaction features between the different modal information, a feature connection diagram is constructed, and initial state information of each modal information in the feature connection diagram is determined by the interaction features between the different modal information; performing iterative updating on the state information of the modal information based on the feature connection diagram to obtain the state information after the iterative updating of the modal information; and determining a counterfeit detection result of the to-be-detected information based on the state information after the modal information is iteratively updated. According to the invention, the accuracy of information forgery detection is improved.

The invention relates to the technical field of computer vision and deep learning, and relates to a deep forgery detection method based on mask supervision. According to the technical scheme, an image is input to a preprocessing module for preprocessing; the processed image is respectively input into a mask supervision module for extracting spatial domain features and a frequency domain feature extraction module for extracting frequency domain features; and the spatial domain features and the frequency domain features are connected and input to a classification module to obtain a final prediction result. The method has the beneficial effects that the attention of the model to the irrelevant region is supervised and corrected through the mask, and a pixel-level label is used for guiding the model to face the synthesis region of the face, so that more accurate extraction of spatial features is ensured; the depth forgery detection method based on mask supervision has remarkable advantages in the aspects of detection accuracy, robustness, generalization ability, calculation efficiency, interpretability, overall performance optimization and the like, and a new solution is provided for the field of depth forgery detection.

The invention provides a face video depth forgery detection method based on face micro-expressions, which comprises the following steps of: 1, extracting face micro-expression actions from a face video to obtain a micro-action time sequence; step 2, performing time sequence consistency learning according to the micro-action time sequence; step 3, based on a GRU network architecture, introducing an attention mechanism, and constructing an authenticity detection model for capturing abnormal features of the micro-action time sequence under a short scale; step 4, training the authenticity detection model; and step 5, carrying out authenticity detection, extracting a human face micro-motion track for a to-be-detected human face video with unknown authenticity, and then detecting time sequence abnormal features by adopting the authenticity detection model trained in the step 4 to obtain a classification result of whether the to-be-detected human face video belongs to a forged video or not. The method is competitive in accuracy and generalization.