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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 161–170 of 204 papers

TitleStatusHype
Re-evaluation of Face Anti-spoofing Algorithm in Post COVID-19 Era Using Mask Based Occlusion Attack—0
Unsupervised Feature Disentanglement and Augmentation Network for One-class Face Anti-spoofing—0
Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Less—0
Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing—0
Rethinking Generalizable Face Anti-spoofing via Hierarchical Prototype-guided Distribution Refinement in Hyperbolic Space—0
Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing—0
Review of Face Presentation Attack Detection Competitions—0
Use of in-the-wild images for anomaly detection in face anti-spoofing—0
RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods—0
Robust face anti-spoofing framework with Convolutional Vision Transformer—0
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