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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 111–120 of 204 papers

TitleStatusHype
Test-Time Domain Generalization for Face Anti-Spoofing—0
Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System—0
Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection—0
Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models—0
Attention-Based Face AntiSpoofing of RGB Images, using a Minimal End-2-End Neural Network—0
Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning—0
Visual Prompt Flexible-Modal Face Anti-Spoofing—0
A Survey On Anti-Spoofing Methods For Face Recognition with RGB Cameras of Generic Consumer Devices—0
Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing—0
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision—0
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