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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.
On Disentangling Spoof Trace for Generic Face Anti-Spoofing
Face Anti-Spoofing with Human Material Perception
Creating Artificial Modalities to Solve RGB Liveness
Use of in-the-wild images for anomaly detection in face anti-spoofing
On Improving Temporal Consistency for Online Face Liveness Detection
mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation
Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach
Federated Face Presentation Attack Detection
3D Face Anti-spoofing with Factorized Bilinear Coding
Learning Generalized Spoof Cues for Face Anti-spoofing
Single-Side Domain Generalization for Face Anti-Spoofing
PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review
Multi-Modal Face Anti-Spoofing Based on Central Difference Networks
Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Deep Frequent Spatial Temporal Learning for Face Anti-Spoofing
Attention-Based Face AntiSpoofing of RGB Images, using a Minimal End-2-End Neural Network
Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing
Static and Dynamic Fusion for Multi-modal Cross-ethnicity Face Anti-spoofing
Regularized Fine-grained Meta Face Anti-spoofing
Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing
CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing
Style Transfer Applied to Face Liveness Detection with User-Centered Models
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Deep Anomaly Detection for Generalized Face Anti-Spoofing
Generalized Presentation Attack Detection: a face anti-spoofing evaluation proposal
Deep Tree Learning for Zero-shot Face Anti-Spoofing
Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection
Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices
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