SOTAVerified

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 101–110 of 204 papers

TitleStatusHype
Mask Attack Detection Using Vascular-weighted Motion-robust rPPG Signals—0
MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing—0
EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing—0
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing—0
Meta-Teacher For Face Anti-Spoofing—0
Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing—0
Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues—0
Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing—0
Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions—0
NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing—0
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