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/BiDAlab/mEBAL
공식 코드mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation
https://github.com/Davidzhangyuanhan/CelebA-Spoof
공식 코드CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations
https://github.com/Faceplugin-ltd/FaceLivenessDetection-Android
공식 코드Generalized Face Liveness Detection via De-fake Face Generator
https://github.com/Garsonjw/AdvFAS
공식 코드AdvFAS: A robust face anti-spoofing framework against adversarial examples
https://github.com/Gradiant/bob.paper.icb2019.gradgpad
공식 코드Generalized Presentation Attack Detection: a face anti-spoofing evaluation proposal
https://github.com/JDAI-CV/faceX-Zoo
공식 코드Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning
https://github.com/KaimingHe/resnet-1k-layers
공식 코드https://github.com/Pei-KaiHuang/AAAI25-SLIP
공식 코드SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining
https://github.com/Pei-KaiHuang/LDCformer_Ext
공식 코드Enhancing Learnable Descriptive Convolutional Vision Transformer for Face Anti-Spoofing
https://github.com/Usman1021/DLE
공식 코드Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
https://github.com/WindQingYang/PipeNet
공식 코드PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
https://github.com/ZamDimon/liveness-detection
공식 코드Cross-Database Liveness Detection: Insights from Comparative Biometric Analysis
https://github.com/ZitongYu/CDCN
공식 코드Searching Central Difference Convolutional Networks for Face Anti-Spoofing
https://github.com/ZitongYu/DeepFAS
공식 코드https://github.com/ai-application-and-integration-lab/dgua_fas
공식 코드https://github.com/anjith2006/bob.paper.cross_modal_focal_loss_cvpr2021
공식 코드https://github.com/anjith2006/bob.paper.deep_pix_bis_pad.icb2019
공식 코드Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/anjith2006/bob.paper.ijcb2021_vision_transformer_pad
공식 코드On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
https://github.com/chaochao-lin/atr-fas
공식 코드Enhancing Mobile Face Anti-Spoofing: A Robust Framework for Diverse Attack Types under Screen Flash
https://github.com/chelsea234/multi-domain-learning-fas
공식 코드Multi-domain Learning for Updating Face Anti-spoofing Models
https://github.com/chenqikong/m3fas
공식 코드M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System
https://github.com/clks-wzz/FAS-SGTD
공식 코드Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
https://github.com/clks-wzz/epcr
공식 코드https://github.com/deepinsight/insightface/tree/master/challenges/cvpr23-fas-wild
공식 코드Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
https://github.com/gurayozgur/foundpad
공식 코드FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection
https://github.com/koushiksrivats/flip
공식 코드FLIP: Cross-domain Face Anti-spoofing with Language Guidance
https://github.com/kunkunlin1221/InstructFLIP
공식 코드InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing
https://github.com/laiyingxin2/shield
공식 코드https://github.com/leminhbinh0209/cvpr24-fas
공식 코드https://github.com/lustoo/fghv
공식 코드Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing
https://github.com/murinj/big-moe
공식 코드BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
https://github.com/murphytju/difffas
공식 코드https://github.com/omggggg/mmdg
공식 코드Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
https://github.com/qianyuzqy/iadg
공식 코드https://github.com/rizhaocai/metapattern_fas
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.