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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 41–50 of 204 papers

TitleStatusHype
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch RecognitionCode1
Deep Change Monitoring: A Hyperbolic Representative Learning Framework and a Dataset for Long-term Fine-grained Tree Change DetectionCode1
CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing—0
A Multi-Modal Approach for Face Anti-Spoofing in Non-Calibrated Systems using Disparity Maps—0
CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing—0
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
3D Face Anti-spoofing with Factorized Bilinear Coding—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
Camera Invariant Feature Learning for Generalized Face Anti-spoofing—0
Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection—0
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