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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 151–160 of 204 papers

TitleStatusHype
Face Anti-Spoofing Via Disentangled Representation Learning—0
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich AnnotationsCode1
Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural NetworksCode0
On Disentangling Spoof Trace for Generic Face Anti-SpoofingCode1
Face Anti-Spoofing with Human Material Perception—0
Creating Artificial Modalities to Solve RGB LivenessCode1
Use of in-the-wild images for anomaly detection in face anti-spoofing—0
On Improving Temporal Consistency for Online Face Liveness Detection—0
mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level EstimationCode1
Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach—0
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