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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 6170 of 204 papers

TitleStatusHype
Joint Statistical and Causal Feature Modulated Face Anti-SpoofingCode0
Latent Distribution Adjusting for Face Anti-SpoofingCode0
Improving Face Anti-Spoofing by 3D Virtual SynthesisCode0
Cross-Database Liveness Detection: Insights from Comparative Biometric AnalysisCode0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
A personalized benchmark for face anti-spoofingCode0
Generalized Face Liveness Detection via De-fake Face GeneratorCode0
Enhancing Learnable Descriptive Convolutional Vision Transformer for Face Anti-SpoofingCode0
Feature Generation and Hypothesis Verification for Reliable Face Anti-SpoofingCode0
Face De-Spoofing: Anti-Spoofing via Noise ModelingCode0
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