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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 171–180 of 204 papers

TitleStatusHype
Robust Face Anti-Spoofing with Dual Probabilistic Modeling—0
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens—0
Saliency-based Video Summarization for Face Anti-spoofing—0
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
Self-Domain Adaptation for Face Anti-Spoofing—0
Semi-Supervised learning for Face Anti-Spoofing using Apex frame—0
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
Shuffled Patch-Wise Supervision for Presentation Attack Detection—0
Adaptive-avg-pooling based Attention Vision Transformer for Face Anti-spoofing—0
3D Face Anti-spoofing with Factorized Bilinear Coding—0
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