2063results about "Spoof detection" patented technology

The invention provides a method and system for preventing identity spoofing during digital authentication processes using artificial intelligence (AI)-driven pattern recognition. The system receives an input data stream from a user attempting to authenticate, which may include biometric data, device behavior data, or user interaction data. An AI-based pattern recognition model processes this data to analyze user behavior patterns and detect any anomalies that may indicate potential spoofing attempts. The system compares the processed data against a pre-established user profile to generate an authentication decision. If anomalies are detected, the system can flag the authentication for further review or trigger additional verification steps, such as multi-factor authentication (MFA) or one-time password (OTP) prompts. The system continuously learns from user interaction data and dynamically updates the user profile to improve the accuracy of identity verification.

A method is provided for improving image classification accuracy in few-shot learning scenarios, where only a limited number of training examples are available. The method combines the use of Gabor filters and convolutional neural networks (CNNs) to extract detailed texture and orientation features from images. These features are then enhanced through global average pooling, aggregated into comprehensive feature vectors, and refined using an attention mechanism that identifies and emphasizes the most relevant features for classification. Masks generated from this attention process selectively enhance critical features, which, after optional re-encoding, are used to train a classifier via a metric learning approach. This method aims to increase feature separability and classification performance, facilitating more accurate classification of new images with minimal training data.

Systems, methods, and computer program products are disclosed for selectively employing a processing mode based on a continuous gesture. Selectively employing a processing mode may include detecting an existence of a mode-selection gesture by an individual. Upon detecting the existence of the mode-selection gesture, a first mode for processing facial micromovements of the individual may be continuously implemented while continuously detecting the mode-selection gesture. Following the continuous detection of the mode-selection gesture, a cessation of the mode-selection gesture may be detected. In response, implementation of the first mode may cease, and implementation of a second mode different from the first mode may be initiated for processing facial micromovements of the individual.

A method for generating a multimodal forensic report using hybrid metric learning and signature-based models is disclosed herein. The method involves receiving and preprocessing sensory data, extracting features using AI models, applying reasoning for anomaly detection and classification, and integrating spatiotemporal, multimodal AI representation learning, and symbolic knowledge. Dynamic domain-specific knowledge is generated by applying data-driven and ontology knowledge to the model. Explanations are generated using unimodal and multimodal reasoning, and associated features are sorted, prioritized, and indexed in a structured format.

The invention discloses a power plant specific area personnel behavior identification and early warning system, and relates to the technical field of industrial safety intelligent monitoring, the power plant specific area personnel behavior identification and early warning system comprises a data acquisition module, an edge calculation module and a central processing platform, the edge computing module carries out lightweight processing and localized decision making on original data, the load of the central processing platform is reduced, and the central processing platform realizes full-process safety management and control of a high-risk area through multi-modal data fusion, dynamic authority management and trajectory analysis. According to the invention, lightweight AI models are embedded in various cameras of a work card authentication terminal and a visual perception unit, preliminary reasoning is directly completed at the work card authentication terminal and the cameras, so that the work card authentication terminal and the cameras have edge computing capability, and independent edge servers or industrial personal computers are deployed near data sources of various regions of a power plant. And serving as a regional edge computing node.

Systems, methods, and computer program products are disclosed for establishing nonvocalized communications. Establishing nonvocalized communications may include detecting a directional gesture made by a wearer of a first wearable device configured to determine facial skin micromovements of the wearer, wherein the directional gesture identifies a second device in proximity to the first wearable device. Based on the directional gesture, a wireless communication channel for enabling a nonvocalized communication between the first wearable device and the second device is selected. Thereafter, specific facial skin micromovements of the wearer of the first wearable device are determined. The specific facial skin micromovements indicate words to be communicated in an absence of perceptible vocalization by the wearer of the first wearable device. Then, the words are transmitted via the wireless communication channel from the first wearable device to the second device for presentation via the second device.

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 power transmission line constructor identity verification method based on face recognition, and relates to the technical field of electric power engineering safety management, and the method comprises the steps: collecting multispectral face data, and generating a spectral stereo feature matrix; micro blood flow pulsation characteristics, skin texture characteristics and thermal imaging temperature distribution characteristics are extracted from the spectrum stereo characteristic matrix, a three-layer cascade anti-counterfeiting verification mechanism is constructed, and comprehensive living body judgment is carried out; establishing a distributed feature matching network, and matching the spectrum stereo feature matrix with a pre-stored feature library; collecting a geographic position track and an operation behavior mode of a constructor, and carrying out multi-dimensional cross validation on the geographic position track and the operation behavior mode and an identity matching result to generate a multi-level safety evaluation index; and writing the identity verification process and the multi-level security evaluation index into a distributed account book, and generating a verification voucher. According to the invention, cross analysis is carried out on the identity matching result of the constructor and the behavior characteristics, so that the problem that a traditional face recognition system is easily falsely used by the identity is effectively solved.

The invention relates to the technical field of audio and video identity recognition, in particular to an audio and video identity recognition system based on multimode clue driving. The audio and video identity recognition system comprises an audio feature extraction module, a video feature extraction module, a multimode clue fusion module, a living body detection module and an identity recognition and verification module. The audio feature extraction module is used for capturing a voice signal of a user and extracting key voiceprint features, the video feature extraction module is used for acquiring facial features or limb features of the user and extracting related features, and the multi-mode clue fusion module is used for performing intelligent weighted fusion on the features of audio and video modes. The method comprises the following steps: firstly, using a living body detection module to comprehensively analyze the dynamic characteristics of audio and video, judging whether a user is a real individual, and finally, completing the final verification of the identity of the user through an identity recognition and verification module. The problem of feature extraction and modal fusion in the prior art is effectively solved.

The invention relates to the technical field of computer vision and pattern recognition, in particular to a deep-forged face image detection method based on a double-flow CNN and ViT hybrid model. The method comprises the following steps: preprocessing a real video and a forged video to obtain an original face image; obtaining a high-frequency noise residual error feature image of the original face image based on an improved high-frequency noise residual error feature extraction module; performing first feature enhancement operation on the original face image and the high-frequency noise residual feature image based on a double-flow CNN; carrying out secondary feature enhancement operation on the original face image and the high-frequency noise residual feature image which are subjected to the primary feature enhancement operation on the basis of double-flow ViT; and fusing the features of the original face image and the high-frequency noise residual feature image after the secondary feature enhancement operation, performing true and false prediction on the fused features, and outputting a detection result. The objective of the invention is to solve the technical problems of feature splitting, local-global imbalance and insufficient cross-modal interaction in the prior art.

The invention discloses a face change detection method, device and equipment based on multi-granularity feature fusion, and the method comprises the steps: carrying out the preprocessing of a data set containing real face data and face change data, and obtaining a preprocessed data set; an Xception model is used as a reference network, a feature extraction layer is improved based on a global-local multi-granularity feature fusion mechanism, a multi-granularity feature fusion framework including coarse-granularity global feature branches and fine-granularity local feature branches is constructed, a convolution attention mechanism is introduced to distribute attention in space and channel dimensions to refine features and perform adaptive optimization, and a multi-granularity feature fusion algorithm is constructed. Therefore, an artificial intelligence face change detection model based on global and local multi-granularity feature fusion is constructed; training an artificial intelligence face change detection model based on the preprocessed data set; and deploying the trained artificial intelligence face change detection model, and performing face change detection on an input face video or image to obtain a face change recognition result. According to the invention, the robustness and accuracy of face change detection can be effectively improved.

The invention provides a deep counterfeit multi-label sorting and positioning method based on multi-domain feature fusion. The deep counterfeit multi-label sorting and positioning method comprises the following steps: preprocessing a sequence deep counterfeit data set; inputting the human face RGB image into a DINOv2-based spatial feature extractor to carry out spatial domain feature extraction; a face RGB image is decomposed through discrete cosine transform to obtain a plurality of frequency domain coefficients and frequency spectrums, the obtained frequency spectrums are flattened and remodeled, components with the same frequency are grouped into a channel to form new input, and then the original color input of the face RGB image is finally converted into frequency domain feature representation through an encoder. The spatial domain features and the frequency domain features are utilized, splicing fusion operation is carried out through a cross attention module, new multi-domain representation of the face deep counterfeit image with the spatial domain and the frequency domain is formed, and then the multi-domain representation is input into a graph attention network to capture the complex relation between different features. According to the method, a more accurate deep counterfeiting analysis result can be realized.

The invention discloses a face recognition system based on computer vision and an application method thereof, and relates to the technical field of face recognition. The system comprises a fine-grained shielding classification module, a multi-modal feature fusion module, a cross-modal confrontation identification module and a compensation verification module. By analyzing the depth data and the polarized light parameters, an occlusion thermodynamic diagram is generated, and an identifiable area and an occlusion area are marked. And according to the occlusion label of the thermodynamic diagram, activating infrared and sonic sensor data, extracting the relevance between the temperature change and the vibration frequency, and generating a facial occlusion area feature map. And calculating the similarity between the sheltered region feature map and the non-sheltered region feature, and detecting a forgery attack. And analyzing the difference rate of adjacent frames through a sliding window, marking compensation distortion, rejecting low-credibility data in combination with confidence and attack marks, and outputting a complete facial feature vector and a risk level. And the recognition accuracy and the system security in a complex environment are remarkably improved.

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

The invention discloses an AI generated face image authenticity discrimination method and device and a storage medium, and belongs to the technical field of computer vision and artificial intelligence security. The invention provides a detection framework based on composite scaling optimization, dynamic enhancement and incremental learning collaboration, depth, width and resolution collaboration expansion of a network architecture is optimized through a composite scaling strategy, and the training intensity is dynamically adjusted in combination with an adaptive data enhancement technology. And an incremental learning mechanism is introduced to realize continuous adaptation and knowledge accumulation of the model to a novel generation technology, so that the detection robustness of a diversified generation technology is remarkably improved while the calculation complexity is reduced. Based on verification of an authoritative test set, the method is superior to an existing method in the aspects of detection precision, generalization ability and continuous adaptability, has a better model efficiency balance characteristic, and provides reliable technical support for digital content security verification.

The technical solution aims to verify digital identity and more particularly to verify digital identity by online proofing.A method for detecting anomalies in identity verification performed by a processor comprises the following steps (FIG. 1, FIG. 2): receiving an identity verification request comprising image data (10), wherein the image data contains a person's face; generating an image data descriptor (11) using a model (20) configured to determine a set of visual features not associated with the person's face in the input image data (10); searching for image data descriptors similar to said generated image data descriptor (11) among image data descriptors belonging to other identity verification requests; in response to finding at least one similar image data descriptor, marking said identity verification request as anomalous, otherwise marking the request as not anomalous.

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.

According to various embodiments, a system and method for verifying a user's identity using both document-based and biometric data is disclosed. The system may prompt a user to upload an image of a government-issued identification document and extract user information from the image using optical character recognition (OCR). The system may also extract the embedded face image from the ID and prompt the user to take a real-time selfie while performing one or more randomized actions or poses. A facial recognition engine may compare the extracted ID image to the live selfie to determine a similarity score. Based on this match, along with optional document authenticity checks, the system may confirm the user's identity in real-time for use cases such as account access, onboarding, and regulatory KYC compliance.

The invention provides a palm vein feature extraction and depth adaptive matching method, which relates to the technical field of biological recognition, and is characterized in that a three-dimensional space-time convolutional neural network and a graph neural network are combined to extract space depth features, time blood flow dynamic features and blood vessel topological features of palm veins, and information bottleneck is utilized to optimize and screen a feature subset with the most distinguishing property; the method breaks through the limitation of traditional two-dimensional texture feature extraction, obtains abundant palm vein information through multi-source data acquisition (such as near-infrared imaging, a light field camera and a spectrum sensor), overcomes the interference of environmental factors such as illumination, angle and noise, achieves the high-precision modeling and stable extraction effects of palm vein features, and can achieve the real-time extraction of the palm vein features in a complex scene. A high recognition rate is kept under different illumination conditions or user hand posture changes, the robustness of the palm vein recognition system is remarkably improved, and the method is suitable for scenes with high safety requirements.

A system and method for multiple fraud type detection includes anti-injection attack system that has a layered architectural approach that uses a includes combination of different specific models to detect the attacks in combination with image processing techniques, device signals and liveness checks to detect the variety of different types of fraud attacks or repeat fraud attacks. The anti-injection attack system applies the analysis of the tools used to create deepfake, face morph and face swap attacks to define the elements of its layered architecture that can these various types of attacks.

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.

Systems, methods, and non-transitory computer-readable media containing instructions are disclosed for detecting and utilizing facial skin micro-motion. In some non-limiting embodiments, detection of facial skin micro-motion occurs using a speech detection system that may include a wearable housing, a light source (coherent light source or incoherent light source), a light detector, and at least one processor. The one or more processors may be configured to analyze light reflections received from the facial region to determine facial skin micromotion, and extract meanings from the determined facial skin micromotion. Examples of meanings that may be extracted from the determined facial skin micromotion may include words spoken by the individual (silent or voiced), an identity of the individual, an emotional state of the individual, a heart rate of the individual, a respiratory rate of the individual, or any other biometric, emotion or speech related indicator.

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.

A method for training a model for classifying videos as real or fake can include generating image tiles and audio data segments from an input video, generating a sequence of image embeddings based on the image tiles using a visual encoder and a sequence of audio embeddings based on the audio data segments using an audio encoder, transforming, using a V2A network, a first subset of the sequence of image embeddings into synthetic audio embeddings, transforming, using an A2V network, a first subset of the sequence of audio embeddings into synthetic image embeddings, updating the sequence of image embeddings by using the synthetic image embeddings, updating the sequence of audio embeddings using the synthetic audio embeddings, training the encoders and the networks using the updated sequences of image embeddings and audio embeddings, and training a classifier using the trained encoders and the trained networks.

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 discloses a forged face image detection method and device. The method comprises the following steps: acquiring a to-be-detected target face image; a pre-trained target face counterfeiting detection model is used to analyze the target face image to obtain a corresponding detection result, and the detection result is used to reflect whether the target face image is a counterfeiting face image or not; the target face forgery detection model is obtained by training an initial face forgery detection model by using a training sample data pair containing a real face image sample and a corresponding forgery face image sample and adopting a phase reaction force mechanism. According to the method and the device, the technical problem that the detection accuracy of the forged face image is relatively low due to the fact that a related face forging detection model cannot accurately learn the forging detail features in the forged face image is solved.

Various aspects of the disclosure relate to automated monitoring of content associated with an individual for indicators that the content may be a deepfake. A deepfake analysis training device trains artificial intelligence-based models based on validated content associated with one or more profiles. Each profile is associated with an individual. A deepfake analysis engine may be installed on computing devices to monitor access to content (e.g., multimedia content) for deepfakes associated with the one or more profiles, as configured. The deepfake analysis engine may be stand-alone application, an application extension, or may be integrated into third-party applications via application programming interface functions. Analyzed content is assigned a likelihood score that is compared to threshold values that indicates a likelihood that the content is a deepfake. When identified, deepfake content initiates an alert generation and / or blocking of the content otherwise the content is presented to a user.

The invention discloses a collaborative anti-interference access control system based on face recognition, and belongs to the technical field of access control systems. The cooperative anti-interference access control system comprises a multi-mode sensing acquisition module, an image preprocessing and anti-interference module, a face recognition and living body verification module, a cooperative control and decision module, a remote management and iteration module and an access control execution module. According to the access control system, the working mode can be adjusted in real time according to environmental changes, the influence of various interference factors is effectively resisted, a more stable and reliable face recognition access control service is provided for a user, the complementarity of multi-source data can be fully utilized by the access control system, the collected target image is optimized and processed, and the user experience is improved. The influence of interference factors such as light and noise on the image is reduced, the reliability and the accuracy of data are effectively improved, and the anti-interference capability and the face recognition accuracy of the system in a complex environment are enhanced.

A remote signal-based method and system of performing an authentication of video of a person in order to authorize access to a secured resource. The system and method are configured to cause a visual and / or audio pattern to be displayed in a remote computing device while collecting image data from the same device. The image data is evaluated to determine whether the appropriate response from the person is present. If the response is present, the system determines the image is authentic and can verify an identity of the person, and can further be configured to automatically grant the person access to one or more services, features, or information for which he or she is authorized.

The invention discloses a face tampering active defense method based on adversarial attack and frequency domain manipulation, and the method comprises the steps: generating adversarial disturbance through a disturbance generator, applying the adversarial disturbance to a face image, and generating a protected face image; respectively inputting the original face picture and the protected face picture into a face tampering model to obtain a tampered face, and calculating confrontation loss and generation loss; training a plurality of teacher disturbance generators for different tampering models; designing frequency sensing loss based on wavelet transform and inverse transform; training a student disturbance generator by using confrontation loss, multi-teacher distillation loss and frequency domain perception loss; a student disturbance generator is used for adding disturbance to the face picture, and active defense is carried out. According to the method, by generating universal and imperceptible adversarial disturbance, various face tampering methods can be effectively defended, and the obvious superiority is particularly shown in a black box scene.