338 results about "Forgery detection" patented technology

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 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 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 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 a source tracking and forgery detection method and system based on a face semantic mask, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring a to-be-processed image; dividing a face semantic core region and a face semantic non-core region of the to-be-processed image, and generating masks of the face semantic core region and the face semantic non-core region; respectively embedding a detection watermark and a traceability watermark into the human face semantic core area and the human face semantic non-core area through a watermark encoder to obtain an image embedded with the detection watermark and the traceability watermark; processing the image embedded with the traceability watermark and the detection watermark through a noise layer to obtain a noise image; a traceability watermark and a detection watermark are extracted from the noise image through a traceability decoder and a detection decoder respectively, and whether the image is deeply counterfeited or not is judged; and carrying out loss function constraint training. According to the invention, the accuracy of image forgery detection can be improved.

The invention discloses a face forgery detection method and system based on multi-view collaborative fusion, and relates to the technical field of computer vision, and the method comprises the steps: obtaining to-be-detected video frame data; preprocessing the video frame data to be detected to obtain a standardized input image tensor; and inputting the standardized input image tensor into a pre-trained face counterfeiting detection model, and processing the standardized input image tensor by the face counterfeiting detection model to generate a face counterfeiting detection result. According to the method, the technical problem of poor detection performance of the model in cross-library and complex environments is solved, the detection robustness of the model in complex scenes such as fuzzy and compressed scenes is remarkably improved, and the detection precision is remarkably improved.

The invention belongs to the technical field of multimedia security, and particularly relates to a deep forgery detection method and system based on audio and video multi-mode fusion. The method comprises the following steps: extracting lip motion space-time characteristics from a video stream through dynamic ROI (Region of Interest) cutting, and processing an audio stream through fast Fourier transform and a Mel filter bank in sequence to obtain audio spectrum characteristics; reconstructing and generating a corresponding audio feature based on the spatio-temporal feature of lip motion, and fusing the audio spectrum feature and the generated audio feature by adopting a bidirectional cross-modal attention mechanism to obtain an attention fusion feature; a Mel spectrogram of the audio stream is acquired, and feature maps of different scales are extracted from the Mel spectrogram and the video stream by using a feature extraction model formed by a convolutional layer and are fused to obtain multi-scale features; after the attention fusion feature and the multi-scale fusion feature are flattened, performing weighted fusion after channel dimension splicing, and outputting a forgery probability through a multi-layer perceptron. The video detection performance is improved.

The invention relates to the technical field of deep counterfeit image detection, and discloses a deep counterfeit detection method and system, a storage medium and computer equipment. The method comprises the following steps: firstly, constructing a reference data set containing a forged image and an original real image; secondly, through an integrated model, generating antagonistic samples for the reference data set, and integrating the successfully attacked antagonistic samples into an antagonistic sample set; and finally, merging the reference data set and the adversarial sample set, and constructing a robustness enhanced data set containing four types of samples. In the model training stage, multi-classification cross entropy loss and comparative learning loss are combined, and expression of the model in a feature space is optimized through comparative learning constraint, so that the model learns discriminative features with more compact intra-class features and more dispersed inter-class features. The model trained by the method not only can effectively defend against attack and improve robustness, but also surpasses original detection performance on clean samples, and has remarkable technical advantages and application value.

The invention provides a video depth forgery detection method based on space-time inconsistency and frequency domain analysis, the forgery detection method constructs a video depth forgery detection network of an unknown environment image, and the video depth forgery detection network comprises a space-time inconsistency module, a frequency domain analysis module and a video level classifier. The space-time inconsistency module captures space-time features in video frames, through a double-flow cooperation mechanism integrating space-time inconsistency modeling and frequency domain artifact analysis, the space-time features in videos are efficiently captured, dynamic information interaction between key frames is achieved, and therefore an enough feature basis is provided for deep counterfeiting detection. And the frequency domain analysis module combines a deep neural network with time-frequency analysis and maps video frames to a frequency domain so as to realize end-to-end identification of an unknown tampering mode and provide support for understanding of fine-grained content and improvement of classification precision.

The embodiment of the invention provides a counterfeit recognition method and device based on local image understanding, and the method comprises the steps: receiving a to-be-recognized image, extracting the features of a counterfeit region of the to-be-recognized image, generating an abnormal region thermodynamic diagram of a suspicious counterfeit region corresponding to the to-be-recognized image based on the features of the counterfeit region, and carrying out the recognition of the abnormal region thermodynamic diagram. Under the condition that the spatial distribution of the abnormal region thermodynamic diagram is reasonable, generating structured text prompt information according to a preset prompt information format based on the abnormal region thermodynamic diagram and the to-be-recognized image, and encoding the text prompt information and the to-be-recognized image to obtain text embedding features, and activating a target expert model corresponding to the suspicious counterfeit region based on the text embedding feature, and processing the corresponding suspicious counterfeit region through the target expert model to obtain a counterfeit confidence coefficient and a counterfeit recognition result corresponding to each suspicious counterfeit region. According to the method, the defect that local counterfeit information is easy to omit is effectively overcome, and the accuracy of counterfeit identification is remarkably improved.

The invention discloses a deep forgery detection method and system based on frequency domain channel selection and mutual information, and belongs to the technical field of image processing. The method comprises the steps that a face image data set is acquired and preprocessed; constructing a deep forgery detection model and performing training by using the preprocessed face image to obtain a trained deep forgery detection model; and obtaining a face image to be subjected to forgery detection, and obtaining the authenticity of the face image to be subjected to forgery detection by using the trained deep forgery detection model. The method not only improves the detection accuracy of multi-type counterfeit techniques, but also has strong compression resistance and cross-dataset generalization ability, and is suitable for security detection tasks of various complex and multi-source counterfeit contents.

The invention discloses a deep forgery detection method and system based on attention suppression and patch recombination, and the method comprises the following steps: dividing an input image into a plurality of non-overlapping image patches, and obtaining a patch feature sequence in combination with a classification mark; constructing a deep forgery detection model and training the deep forgery detection model; sorting the attention weights, randomly inhibiting the image patches of the first k weights, performing dichotomy authenticity prediction based on the classification mark of the complete image, and storing the original attention weight before inhibition; the patch feature sequence is divided and recombined to form two new patch groups, the two new patch groups are respectively input into a trunk vision Transform network which shares weights but closes an attention suppression module, classification mark features are extracted, and the classification mark features and the classification mark features of a complete image are subjected to comparative learning; and based on the trained trunk vision Transform network, extracting to obtain a deep counterfeiting detection feature. The method has efficient forgery detection performance in a complex and changeable environment.

The invention relates to the technical field of audio forgery detection, in particular to an audio forgery detection method and system based on lightweight federated adversarial training, and the method comprises the steps: a client extracts audio features through a lightweight network, and updates a model through local adversarial training; the server aggregates the client parameters to generate a global model and issues the global model; and the client performs forgery detection by using the global model. According to the method, a federal learning framework is adopted, original audio is not out of the local, only compressed model parameters are uploaded, high-performance collaborative learning under privacy protection is realized, a data island is broken, and the generalization ability is improved; time domain, frequency domain and frequency spectrum residual multi-dimensional features are fused with cross-modal attention, and adversarial disturbance training is introduced locally, so that feature resolution and anti-interference robustness are enhanced; through combination of pruning, quantification and distillation lightweight processing, the communication and calculation overhead is remarkably reduced, and mobile and IoT edge equipment can be efficiently deployed.

A multi-branch DLMDMLP and adversarial generation deep counterfeit detection method belongs to the technical field of deep counterfeit monitoring and multimedia security, and comprises the following steps: obtaining a counterfeit image or video data set, and carrying out data preprocessing; the method comprises the following steps of: improving a DeepFake-Adapter model, and obtaining an improved KaleidoDynAdv DeepFake Desection depth forgery detection model; the KaleidoDynAdv DeepFake Desection depth forgery detection model obtained after improvement in the second step is trained, and the KaleidoDynAdv DeepFake Desection depth forgery detection model obtained after improvement in the second step is trained; and carrying out forgery detection by adopting the trained deep forgery detection model. According to the method, training is carried out by using an open-source data set, so that the deep counterfeit detection discrimination capability AUC, the accuracy accuracy ACC and generalization are improved, the equal error rate EER is reduced, and the social safety is improved.

The invention relates to the technical field of deep faking attribution and detection methods, in particular to a deep faking attribution and detection method based on a gaze guide CLIP model, and the method specifically comprises the following steps: collecting face images, constructing a deep faking attribution and detection data set, enabling each face image in the data set to have a corresponding faking attribution label and a faking detection label, preprocessing images in the data set, and then dividing into a training set and a test set; constructing a zero-sample deep counterfeit attribution and detection model, inputting the preprocessed data set into the model, and performing model processing to obtain predicted image counterfeit detection category features and predicted image counterfeit attribution category features; and performing optimization training on the deep counterfeit attribution and detection model through the training set to obtain an optimized and trained model, and testing the optimized and trained model by using data in the test set. According to the invention, the source of the deep pseudo image is traced through the deep learning method, and the faked attribution and detection can be carried out more accurately.

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.

Implementations are described herein for detecting deepfakes in digital media while preserving the privacy of the source computing device. In various implementations, sensor fingerprints and / or security tokens that signal software-introduced alterations, e.g., introduced by hardware abstraction layers (HALs) or virtual machines (VMs) may be utilized to detect such deepfakes. These signals may be used, separately and / or in combination, for various purposes, such as flagging digital content to a user as being a deepfake, preventing or blocking receipt and / or playback of digital content deemed to be a deepfake, allowing an end user to disable aspect(s) (e.g., layers) of digital content that are determined to be synthetic, etc.

This application relates to a face image processing method, apparatus, computer device, and storage medium. The method includes acquiring a first face image and a second face image, the first face image and the second face image being images of real faces; generating a first updated face image with non-real face image characteristics based on the first face image; adjusting color distribution of the first updated face image according to color distribution of the second face image to obtain a first adjusted face image; acquiring a target face mask of the first face image, the target face mask being generated by randomly deforming a face region of the first face image; and blending the first adjusted face image and the second face image according to the target face mask to obtain a target face image. Accordingly, a diversity of target face images can be generated.

The invention discloses a false information intelligent detection and traceability method based on cross-modal consistency verification. The method specifically comprises the following steps: S1, multi-modal content feature extraction; s2, carrying out cross-modal consistency verification; s3, performing deep counterfeiting detection; s4, propagation anomaly detection; s5, checking knowledge enhancement facts; s6, information tracing and variation tracking; s7, comprehensive authenticity evaluation; s8, a model training strategy; a multi-task learning framework is adopted, and parameters of all modules are jointly optimized. According to the method, a multi-dimensional consistency verification system is constructed by fusing multi-source information such as video content, text semantics, a propagation network and an external knowledge base, and automatic identification, authenticity evaluation and information traceability of various false information are realized.

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 payment security, and particularly discloses a security payment system and method based on biological recognition. The method is characterized by comprising the following steps: synchronously acquiring fingerprint, finger vein and pressure behavior characteristics through a coaxial integrated sensor; a dynamic encryption engine is adopted to bind the biological characteristics with the transaction parameters to generate a one-time payment token; living body verification is realized based on physiological synchronism of vein pulsation and pressure fluctuation; and establishing a user pressing behavior baseline model to identify abnormal operation. The terminal equipment is provided with a sapphire microlens array and a dynamic pressure-sensitive array. The problems of biological feature forgery, replay attack and living body cheating are solved, the forgery detection rate reaches 99.6%, and the false identification rate is smaller than or equal to 0.0001%.

The invention discloses a deep forgery detection and positioning method and system, a storage medium and equipment, and belongs to the field of data detection.The method comprises the steps that image and text data in an image text pair are preprocessed, and structured input data are generated; embedding and mapping the preprocessed image text pair into the same semantic space through a vision-language model; performing alignment by using cross-modal contrast learning, and realizing interaction between the image and the text through a cross attention mechanism; dynamically adjusting the weight of the image and the text modality by adopting a weighted fusion strategy; analyzing the fused modal features by using a classification model, judging the authenticity of the information, and positioning false parts in the image text pairs; and training the model by minimizing a detection error and a loss function, and optimizing the model in combination with a regularization technology. According to the method, based on a weighted fusion strategy, the modality with scarce information can get more attention in the model learning process, so that the overall detection performance is improved.

The invention discloses an access control authentication method based on multi-modal dynamic challenge and block chain auditing, and belongs to the technical field of access control authentication, and the access control authentication method comprises the following steps: collecting video, sound and motion data of a user, and carrying out dynamic living body detection; after detection is passed, face features, voiceprint features and gait features are extracted and subjected to fusion scoring, and after the score exceeds a threshold value, equipment trust chain verification is carried out according to a dynamic token generated after secret key exchange between the APP and the access control terminal; after the verification is passed, firstly performing deep counterfeiting detection on the whole face, and then performing deep counterfeiting detection on the details of the face; and finally, the authentication passing information is stored in the block chain, TxID verification is carried out when a receipt of certificate storage transaction success returned by the block chain is received, and the door is authorized to be opened after the verification is successful. According to the invention, forgery attacks can be effectively resisted, the false identification rate and the missed identification rate of the access control system are reduced, the auditing tracking and tamper-proof capabilities are greatly enhanced, and the identity authentication reliability in a high-security scene is guaranteed.

The invention relates to a face counterfeiting detection method based on a multi-dimensional fusion attention network, and the method employs a multi-dimensional feature extraction framework for human counterfeiting recognition, and comprises the steps: carrying out the preprocessing of a to-be-detected face video, carrying out the sampling at equal intervals, and cutting a face region of each frame of sampling image; the face region is input into a multi-dimensional feature extraction framework, multi-scale features are extracted, and the multi-scale features comprise local five sense organ texture features, global face structure features, deep semantic features and multi-scale perception features; fusing the multi-scale features by using a feature fusion mechanism driven by double attention to obtain fused features; and after channel compression is performed on the fusion features, inputting the fusion features into a discriminator to obtain a face counterfeiting detection result. Compared with the prior art, the accuracy and the anti-interference capability of overall counterfeiting detection are improved.