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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 31–40 of 204 papers

TitleStatusHype
On Disentangling Spoof Trace for Generic Face Anti-SpoofingCode1
Creating Artificial Modalities to Solve RGB LivenessCode1
mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level EstimationCode1
Learning Generalized Spoof Cues for Face Anti-spoofingCode1
Single-Side Domain Generalization for Face Anti-SpoofingCode1
PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-SpoofingCode1
Multi-Modal Face Anti-Spoofing Based on Central Difference NetworksCode1
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofingCode1
Searching Central Difference Convolutional Networks for Face Anti-SpoofingCode1
Exploiting temporal and depth information for multi-frame face anti-spoofingCode1
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