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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 131–140 of 204 papers

TitleStatusHype
Unsupervised Compound Domain Adaptation for Face Anti-Spoofing—0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Dual-Cross Central Difference Network for Face Anti-SpoofingCode1
Bi-FPNFAS: Bi-Directional Feature Pyramid Network for Pixel-Wise Face Anti-Spoofing by Leveraging Fourier Spectra—0
A Dataset and Benchmark Towards Multi-Modal Face Anti-Spoofing Under Surveillance Scenarios—0
A-DeepPixBis: Attentional Angular Margin for Face Anti-Spoofing—0
Cross Modal Focal Loss for RGBD Face Anti-SpoofingCode1
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and ResultsCode1
Self-Domain Adaptation for Face Anti-Spoofing—0
Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection—0
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