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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.
Instance-Aware Domain Generalization for Face Anti-Spoofing
Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Less
Liveness score-based regression neural networks for face anti-spoofing
EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing
Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing
M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System
Surveillance Face Anti-spoofing
Cyclically Disentangled Feature Translation for Face Anti-spoofing
Face Presentation Attack Detection
Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing
Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss
Race Bias Analysis of Bona Fide Errors in face anti-spoofing
Forensicability Assessment of Questioned Images in Recapturing Detection
Face Anti-Spoofing from the Perspective of Data Sampling
Multi-domain Learning for Updating Face Anti-spoofing Models
Learning Facial Liveness Representation for Domain Generalized Face Anti-spoofing
Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues
Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing
Post-Train Adaptive MobileNet for Fast Anti-Spoofing
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing
Generative Domain Adaptation for Face Anti-Spoofing
Online Adaptive Personalization for Face Anti-spoofing
Generalizable Method for Face Anti-Spoofing with Semi-Supervised Learning
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models
A Novel Face-Anti Spoofing Neural Network Model For Face Recognition And Detection
Robust Face Anti-Spoofing with Dual Probabilistic Modeling
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition
Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
Learning Multiple Explainable and Generalizable Cues for Face Anti-spoofing
Flexible-Modal Face Anti-Spoofing: A Benchmark
Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing
Deep Learning meets Liveness Detection: Recent Advancements and Challenges
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