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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>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning
Consistency Regularization for Deep Face Anti-Spoofing
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks
Meta-Teacher For Face Anti-Spoofing
Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofing
Learning Meta Pattern for Face Anti-Spoofing
Shuffled Patch-Wise Supervision for Presentation Attack Detection
Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing
Structure Destruction and Content Combination for Face Anti-Spoofing
Domain Generalization with Pseudo-Domain Label for Face Anti-Spoofing
Dual Reweighting Domain Generalization for Face Presentation Attack Detection
Deep Learning for Face Anti-Spoofing: A Survey
Few-Shot Domain Expansion for Face Anti-Spoofing
Unsupervised Compound Domain Adaptation for Face Anti-Spoofing
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing
Dual-Cross Central Difference Network for Face Anti-Spoofing
A Dataset and Benchmark Towards Multi-Modal Face Anti-Spoofing Under Surveillance Scenarios
Cross Modal Focal Loss for RGBD Face Anti-Spoofing
A-DeepPixBis: Attentional Angular Margin for Face Anti-Spoofing
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results
Self-Domain Adaptation for Face Anti-Spoofing
Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection
Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System
Camera Invariant Feature Learning for Generalized Face Anti-spoofing
Physics-Guided Spoof Trace Disentanglement for Generic Face Anti-Spoofing
Suppressing Spoof-irrelevant Factors for Domain-agnostic Face Anti-spoofing
Uncertainty-Aware Physically-Guided Proxy Tasks for Unseen Domain Face Anti-spoofing
Revisiting Pixel-Wise Supervision for Face Anti-Spoofing
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing
A Survey On Anti-Spoofing Methods For Face Recognition with RGB Cameras of Generic Consumer Devices
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing
Face Anti-Spoofing Via Disentangled Representation Learning
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations
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