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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 51–60 of 204 papers

TitleStatusHype
Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection—0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture—0
Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofing—0
Deep Transfer Across Domains for Face Anti-spoofing—0
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models—0
Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues—0
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