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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 71–80 of 204 papers

TitleStatusHype
A Closer Look at Geometric Temporal Dynamics for Face Anti-Spoofing—0
Mask Attack Detection Using Vascular-weighted Motion-robust rPPG Signals—0
Latent Distribution Adjusting for Face Anti-SpoofingCode0
FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing—0
MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing—0
Surveillance Face Presentation Attack Detection Challenge—0
Instance-Aware Domain Generalization for Face Anti-SpoofingCode1
Wild Face Anti-Spoofing Challenge 2023: Benchmark and ResultsCode0
Rethinking Domain Generalization for Face Anti-spoofing: Separability and AlignmentCode1
Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Less—0
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