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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 21–30 of 204 papers

TitleStatusHype
A Multi-Modal Approach for Face Anti-Spoofing in Non-Calibrated Systems using Disparity Maps—0
DiffFAS: Face Anti-Spoofing via Generative Diffusion ModelsCode1
Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data SynthesisCode0
Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning—0
Re-evaluation of Face Anti-spoofing Algorithm in Post COVID-19 Era Using Mask Based Occlusion Attack—0
G^2V^2former: Graph Guided Video Vision Transformer for Face Anti-Spoofing—0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
Liveness Detection in Computer Vision: Transformer-based Self-Supervised Learning for Face Anti-Spoofing—0
Advancing Cross-Domain Generalizability in Face Anti-Spoofing: Insights, Design, and Metrics—0
Principles of Designing Robust Remote Face Anti-Spoofing Systems—0
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