272 results about "Forgery detection" patented technology

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 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 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.

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 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 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 discloses a multi-dimensional collaborative counterfeit feature detection method for digital content, which comprises the following steps of: S1, receiving the digital content to be analyzed, performing format analysis and image extraction on the digital content, and performing standardization processing on the extracted image data; and S2, carrying out multi-dimensional atomic forgery feature extraction on the standardized image data, detecting potential forgery traces of each dimension, and outputting a preliminary analysis result of each dimension. The invention provides a multi-dimensional collaborative counterfeited feature detection method for digital contents, which integrates counterfeited indication information from various sources through multi-dimensional feature extraction and innovative collaborative analysis and context sensing mechanisms, and performs association analysis on the information in an innovative manner, so that the counterfeited counterfeited information is obtained. Therefore, the detection capability of digital content tampering and the interpretability of the result are effectively improved, the accuracy, robustness and interpretability of complex forgery detection are improved, and the feasibility of implementation is considered at the same time.

The invention relates to an image detection method based on multi-contrast learning, belongs to the technical field of image processing and deep learning, solves the problem that a forgery detection method in the prior art is poor in generalization ability when processing unknown forgery types, and comprises the following steps: S1, establishing a detection model comprising a feature extraction module, performing data preprocessing and feature extraction on the input image to obtain a feature map; s2, establishing a comparative learning module, and pre-training the detection model to obtain a pre-trained detection model; s3, establishing a distillation module which comprises a teacher model and a student model, and transferring knowledge of the teacher model to the student model; s4, establishing a multi-loss joint optimization module, and performing joint optimization in combination with the classification loss, the comparison loss and the distillation loss to obtain an optimized student model; s5, establishing an output prediction module, and generating and outputting a detection result; and S6, inputting the to-be-detected image into the image detection model to obtain a face detection result.

The invention relates to the technical field of image processing, in particular to an image depth forgery detection method, system and equipment, and solves the problems of weak generalization, coarse positioning and no explanation of a traditional method by combining a large-scale visual language model (VLM), a guard context module and a segmentation progressive aggregation module. And multi-region accurate detection, mask positioning and explainable output are realized.

The embodiment of the invention provides a face forgery detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a pre-training detection model and a to-be-detected face image, and inputting the to-be-detected face image into the pre-training detection model, so that a face key point detector obtains a face key point graph according to the input to-be-detected face image; a semantic segmentation graph generator generates a semantic segmentation graph according to the face key point graph; the feature extractor performs basic feature extraction according to the to-be-detected face image to obtain basic features; the global feature extraction branch performs global feature extraction according to the basic features to obtain a global feature map; the feature learning branch obtains a region-level forgery confidence map according to the basic features and the semantic segmentation map; the feature fusion device carries out fusion processing according to the global feature map and the region-level forged confidence map to obtain classification features; and the classifier obtains a face forgery detection result according to the classification features. According to the invention, the accuracy of face forgery detection is improved.

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 discloses a video depth forgery detection method and system based on unsupervised learning, and the method comprises the steps: extracting frames from a training set according to a set interval to construct a frame image set, and carrying out the clustering of the constructed frame image set based on the texture artifacts of a gray-level co-occurrence matrix, extracting frequency domain features of the clustered frame image by using hierarchical discrete wavelet transform, constructing an attention map by using the obtained frequency domain features to perform RGB domain feature enhancement and model training, performing counterfeit recognition on the to-be-recognized image by using the trained model, obtaining the inter-frame similarity in the to-be-recognized image, and performing counterfeit recognition on the to-be-recognized image by using the inter-frame similarity in the to-be-recognized image. According to the method, deep counterfeit classification is carried out according to the obtained similarity, the problems of high labeling cost and cross-domain failure of a supervision model in a traditional method are solved by exploring the internal difference of data and automatically generating a pseudo label, generalization detection can still be effectively carried out under the condition that a large amount of labeling data does not exist, and the detection efficiency is improved. The dependence on manual annotation is greatly reduced, and the applicability of the model in different fields and scenes is enhanced.

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 discloses a unified unsupervised depth forgery detection method based on prototype guidance and double hyperspheres, and the method comprises the following steps: S1, extracting visual artifact features generated by depth forgery, and achieving the generation of pseudo labels through the clustering of a Gaussian mixture model; s2, performing comparative learning through a category prototype of momentum updating; and S3, realizing effective fusion of a feature space and a geometric decision by respectively constructing independent hyper-spheres for real and forged samples, and constructing a dual-depth support vector data description framework. The system has the beneficial effects that the system is composed of three core modules: a visual artifact feature-based pseudo label generator provides a reliable supervision signal, and the visual artifact feature-based pseudo label generator provides a visual artifact feature-based pseudo label description framework; a prototype guided contrast learning (PGCL) module enhances the discrimination capability through a prototype of momentum update, and a dual-depth support vector data description (Dual-DeepSVDD) module constructs a dual-hyperspherical decision boundary of true and false samples, thereby realizing effective integration of feature learning and geometric decision.

The invention discloses a face forgery detection method and device based on multistage joint discrimination and a storage medium, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected face image; inputting the face image to be detected into a pre-trained authenticity detection model to obtain an authenticity prediction result; wherein the authenticity detection model comprises a coding module which is used for generating pixel-level features, region-level features and semantic-level features according to an input face image; the multi-level feature extraction module is used for respectively generating a pixel-level counterfeit feature, a region-level counterfeit feature and a semantic-level counterfeit feature according to the pixel-level feature, the region-level feature and the semantic-level feature; and the classifier is used for obtaining an authenticity prediction result of the input face image according to the spliced pixel-level counterfeit features, the region-level counterfeit features and the semantic-level counterfeit features, and robust and efficient multi-level joint detection of face counterfeit is realized through multi-level counterfeit trace discrimination.