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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 1120 of 204 papers

TitleStatusHype
Deep Change Monitoring: A Hyperbolic Representative Learning Framework and a Dataset for Long-term Fine-grained Tree Change DetectionCode1
Cyclically Disentangled Feature Translation for Face Anti-spoofingCode1
Deep Learning for Face Anti-Spoofing: A SurveyCode1
Exploiting temporal and depth information for multi-frame face anti-spoofingCode1
Dual-Cross Central Difference Network for Face Anti-SpoofingCode1
DiffFAS: Face Anti-Spoofing via Generative Diffusion ModelsCode1
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofingCode1
Domain Generalization via Shuffled Style Assembly for Face Anti-SpoofingCode1
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich AnnotationsCode1
Cross Modal Focal Loss for RGBD Face Anti-SpoofingCode1
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