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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 7180 of 204 papers

TitleStatusHype
Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing0
A visualization method for data domain changes in CNN networks and the optimization method for selecting thresholds in classification tasksCode0
FaceCat: Enhancing Face Recognition Security with a Unified Diffusion Model0
Unified Physical-Digital Attack Detection Challenge0
Test-Time Domain Generalization for Face Anti-Spoofing0
CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing0
Cross-Database Liveness Detection: Insights from Comparative Biometric AnalysisCode0
Generalized Face Liveness Detection via De-fake Face GeneratorCode0
Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing0
Adaptive-avg-pooling based Attention Vision Transformer for Face Anti-spoofing0
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