2771results 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 non-transitory computer readable media including instructions for interpreting facial skin micromovements are disclosed. An example head mountable system includes a housing configured to be worn on a head of a wearer. The head mountable system also includes at least one detector integrated with the housing and configured to receive light reflections from a facial region of the head and to output associated reflection signals. The head mountable system also includes at least one microphone associated with the housing and configured to capture sounds produced by the wearer and to output associated audio signals. The head mountable system also includes and at least one processor in the housing, configured to use both the reflection signals and the audio signals to generate output that corresponds with words articulated by the wearer.

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 present disclosure provides a detection method for low-quality fake face images based on domain-invariant feature learning, including the following steps: S1, inputting datasets into a residual network (ResNet)-50, and forward propagating low-quality compressed input images and original images corresponding thereto; S2, obtaining high-frequency information of two datasets of different resolutions; and using the high-frequency information to compensate for features discarded in the compression process; and S3, performing T-distributed stochastic neighbor embedding (T-SNE) operation on outputs from the ResNet-50 at a final stage to obtain feature distributions of a high-quality dataset and a low-quality dataset. Domain invariance is used for inscribing correct artificial artifact traces, alleviating the overfitting on low-quality compressed images.

The invention discloses a cross-domain face anti-counterfeiting detection method and device based on multi-modal text enhancement, and relates to the technical field of network information security, and the method comprises the steps: inputting two types of description texts into a pre-trained text encoder, and extracting text type features representing reality / deception, inputting the image into a pre-trained visual encoder to extract visual features; a trainable text prompt is added to each layer of the text encoder, a trainable visual prompt is added to each layer of the visual encoder, and the visual prompt of each layer of the visual encoder is obtained by converting the text prompt of the current layer through a full connection layer; and embedding the PFT module and the TIM module into a middle layer of each layer of a text encoder and a visual encoder to realize feature interaction fusion, obtaining cosine similarity and masks between text category features and visual features, and performing face true and false classification. Based on the PFT module and the TIM module, modal feature interaction is completed in the feature extraction process at the same time, and the cross-domain detection performance is improved.

The invention discloses a 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 discloses a deep learning-based deep forged compressed face image identification method, which belongs to the field of artificial intelligence image identification, and comprises the following steps of: 1, carrying out frequency domain modal extraction on a deep forged compressed face image by adopting dynamic adaptive discrete cosine transform; 2, a multi-head self-attention mechanism and a multi-dimensional entropy state load mutual feedback mechanism are adopted to extract frequency domain high-dimensional load features; step 3, extracting spatial domain features based on the pre-trained deep residual network; 4, performing feature fusion on the frequency domain high-dimensional load features and the spatial domain features based on an attention mechanism; and 5, discriminating and classifying the fusion features by using a classifier comprising a full connection layer. According to the method, high-accuracy identification can be carried out on the compressed face image manufactured by adopting a deep counterfeiting means.

The invention provides a deep counterfeit detection method based on self-mixing, and compared with the prior art, the method generates a mixed image containing counterfeit artifacts by using a self-mixing technology, and carries out the precise detection and recognition of an artifact region of an input image through combining a backbone network OfficientNet-b3 and a counterfeit artifact detection module. The method comprises the following steps of: firstly, performing framing processing on a real image, generating a source image and a target image by utilizing a self-mixing technology, and generating a mixed image with counterfeit artifacts by combining a mask generator with an alpha blowing model; and then, the backbone network extracts global features from the mixed image, and the fake artifact detection module reinforces local feature expression of a mixed region through an artifact space cooperation attention module, so that an artifact region is accurately positioned. And finally, fusing global and local features by using residual connection, and transmitting the fused features to a full connection layer to complete classification. According to the method, various forged image types can be effectively detected, and excellent generalization ability is shown in cross-dataset and unknown scenes.

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

The invention 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 dead chicken detection method based on image fusion and improved YOLOv9, and the method comprises the steps: firstly achieving the fusion of a thermal infrared image and a visible light image through a PIAFusion technology, so as to improve the significance of a dead chicken target feature, and eliminate the influence of uneven illumination; secondly, an EMA attention mechanism is introduced into the neck of the YOLOv9 to effectively distinguish a target and a dead chicken target in a low-light environment, so that the accuracy and generalization ability of the model are improved; a Rep-DCNv3 module is introduced to enhance a trunk feature extraction network, and dead chicken features under the condition of partial shielding are better extracted; and finally, replacing a loss function of YOLOv9 with MPDIOU, and evaluating the overlapping degree between the targets more accurately. The dead chicken detection method provided by the invention solves the problems of low automation degree of dead chicken inspection, time and labor waste of manual inspection and the like in a large-scale breeding environment, not only ensures high-efficiency detection speed, but also remarkably improves the precision of target detection, and can meet the requirement for real-time detection of dead chickens in actual production.

The invention provides a multi-modal face in-vivo detection method for image-text contrast learning, a medium and equipment. The method comprises the following steps of: realizing human face living body detection by adopting a CLIP network architecture; the CLIP network architecture comprises a text encoder, an image encoder and a multi-modal information fusion module; expanding the true and false face labels into a text prompt set, and sending the text prompt set into a text encoder to obtain text features; respectively inputting the three modals of the face image into an image encoder to extract image features; calculating cosine similarity between the three modal image features and the text features to obtain classification probabilities; if any modal classification probability confidence coefficient is high, a detection result is directly obtained; and if the confidence coefficients of the three modalities are not high, fusing the image features of the three modalities, and calculating the cosine similarity with the text features to obtain a detection result. According to the method, cross-modal knowledge contained in a CLIP pre-training model is fully utilized, basic features of face images and text prompts are extracted, and the generalization ability of the model is improved.

The invention discloses a face anti-fraud model training method and device based on image-text comparison assisted learning, and belongs to the technical field of artificial intelligence. The method comprises the following steps: identifying the content of each sample image in an image set to obtain a description text corresponding to each sample image; generating text data corresponding to each sample image in combination with the description text and the label; and inputting each sample image in the image set into an image encoder in the multi-modal pre-training model, and inputting text data corresponding to each image into the text encoder in the multi-modal pre-training model, so as to train the multi-modal pre-training model and obtain a face anti-fraud model. According to the method, the image content description text and the face type label are integrated, the features are mixed, the central difference convolution adapter module is introduced to capture the fine features of the face, the features are subjected to semantic alignment through image-text comparison learning and matching, and the generalization ability of the model in different application scenes is improved.

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

The invention discloses a 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 discloses a domain generalization face anti-counterfeiting method for guiding CLIP through cross-modal fusion. The method comprises the steps of inputting an image sample into a preprocessing unit for preprocessing, including face detection and data enhancement of a detected face image; generating text features including category description and content description for each text sample based on the fine-grained prompt template unit, generating visual features for the preprocessed image samples based on the domain perception adapter unit, fusing the text features and the visual features to form cross-modal joint feature representation, and extracting the cross-modal joint feature representation. Training a face anti-counterfeiting prediction network by using joint feature representation; and inputting the real face image processed by the preprocessing unit into the trained face anti-counterfeiting prediction network, and performing reasoning prediction. According to the method, the visual features are dynamically adjusted by using the text features instead of directly editing the visual features, so that the understanding and association of the model on content and category semantics are enhanced, and the domain generalization ability of a face anti-counterfeiting technology is enhanced.

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.

A video detection method includes extracting a plurality of video snippets of a target video, and extracting local features corresponding to the video snippets, respectively, based on motion information of a target object in the video snippets. The local features are configured for representing a time sequence inconsistency of the video snippets. The method further includes performing fusion on the local features to obtain a global feature of the target video, determining an authenticity probability of the target object in the target video based on the global feature, and obtaining a detection result of the target video based on the authenticity probability.

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.