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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
Uncertainty-Aware Physically-Guided Proxy Tasks for Unseen Domain Face Anti-spoofing—0
A Multi-Modal Approach for Face Anti-Spoofing in Non-Calibrated Systems using Disparity Maps—0
Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing—0
Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection—0
Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions—0
NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing—0
Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models—0
Advancing Cross-Domain Generalizability in Face Anti-Spoofing: Insights, Design, and Metrics—0
On Improving Temporal Consistency for Online Face Liveness Detection—0
Online Adaptive Personalization for Face Anti-spoofing—0
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