Face Anti-Spoofing
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Facial anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person’s face. Some examples of attacks:
Print attack: The attacker uses someone’s photo. The image is printed or displayed on a digital device.
Replay/video attack: A more sophisticated way to trick the system, which usually requires a looped video of a victim’s face. This approach ensures behaviour and facial movements to look more ‘natural’ compared to holding someone’s photo.
3D mask attack: During this type of attack, a mask is used as the tool of choice for spoofing. It’s an even more sophisticated attack than playing a face video. In addition to natural facial movements, it enables ways to deceive some extra layers of protection such as depth sensors.
<span style="color:grey; opacity: 0.6">( Image credit: Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Mind23-2/MindCode-31
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-4/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-67
https://github.com/Mind23-2/MindCode-70
https://github.com/MindCode-4/code-4/tree/main/rembert
https://github.com/MindSpore-paper-code-2/code2/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MindSpore-paper-code-2/code3/tree/main/STPM
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet34
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code5/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MioChiu/ResNet_TensorFlow
https://github.com/MisaOgura/flashtorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MishaLaskin/vq-vae
https://github.com/MishaLaskin/vqvae
https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/NASA-NeMO-Net/NeMO-Net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/NVIDIA/retinanet-examples
https://github.com/OlafenwaMoses/Action-Net
https://github.com/OlafenwaMoses/IdenProf
https://github.com/OlafenwaMoses/Traffic-Net
https://github.com/Olayemiy/Image-Classification-With-Resnet
https://github.com/PPPrior/i3d-pytorch
https://github.com/PaddlePaddle/PaddleClas
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/Podidiving/lgsc-for-fas-pytorch
https://github.com/PolyArch/fp-diannao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/Puzer/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Rakshit-Shetty/Resnet-Implementation
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
https://github.com/Recognito-Vision/Face-SDK-Linux-Demos
Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.