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Face Anti-Spoofing

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.

( Image credit: Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing )

Papers

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Showing 161–170 of 204 papers

TitleStatusHype
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review—0
DADM: Dual Alignment of Domain and Modality for Face Anti-spoofing—0
Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing—0
Deep Frequent Spatial Temporal Learning for Face Anti-Spoofing—0
Deep Learning meets Liveness Detection: Recent Advancements and Challenges—0
A Dataset and Benchmark Towards Multi-Modal Face Anti-Spoofing Under Surveillance Scenarios—0
Deep Transfer Across Domains for Face Anti-spoofing—0
A-DeepPixBis: Attentional Angular Margin for Face Anti-Spoofing—0
Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing—0
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