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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 51–60 of 204 papers

TitleStatusHype
TeG-DG: Textually Guided Domain Generalization for Face Anti-Spoofing—0
Presentation Attack Detection using Convolutional Neural Networks and Local Binary Patterns—0
Domain-Generalized Face Anti-Spoofing with Unknown AttacksCode1
Fine-Grained Annotation for Face Anti-Spoofing—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images—0
FLIP: Cross-domain Face Anti-spoofing with Language GuidanceCode1
Distributional Estimation of Data Uncertainty for Surveillance Face Anti-spoofing—0
Semi-Supervised learning for Face Anti-Spoofing using Apex frame—0
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical TokensCode0
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