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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

Showing 141150 of 204 papers

TitleStatusHype
Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing0
Suppressing Spoof-irrelevant Factors for Domain-agnostic Face Anti-spoofing0
Surveillance Face Anti-spoofing0
Surveillance Face Presentation Attack Detection Challenge0
TeG-DG: Textually Guided Domain Generalization for Face Anti-Spoofing0
Test-Time Domain Generalization for Face Anti-Spoofing0
Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning0
CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing0
Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices0
Towards Unsupervised Domain Generalization for Face Anti-Spoofing0
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