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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 141–150 of 204 papers

TitleStatusHype
Shuffled Patch-Wise Supervision for Presentation Attack Detection—0
Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing—0
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
Structure Destruction and Content Combination for Face Anti-Spoofing—0
Domain Generalization with Pseudo-Domain Label for Face Anti-Spoofing—0
Dual Reweighting Domain Generalization for Face Presentation Attack Detection—0
Few-Shot Domain Expansion for Face Anti-Spoofing—0
Unsupervised Compound Domain Adaptation for Face Anti-Spoofing—0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Bi-FPNFAS: Bi-Directional Feature Pyramid Network for Pixel-Wise Face Anti-Spoofing by Leveraging Fourier Spectra—0
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