SOTAVerified

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 26–50 of 204 papers

TitleStatusHype
Deep Learning for Face Anti-Spoofing: A SurveyCode1
Dual-Cross Central Difference Network for Face Anti-SpoofingCode1
Cross Modal Focal Loss for RGBD Face Anti-SpoofingCode1
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and ResultsCode1
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich AnnotationsCode1
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
Very Deep Convolutional Networks for Large-Scale Image RecognitionCode1
Learn Convolutional Neural Network for Face Anti-SpoofingCode1
Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions—0
Leveraging Intermediate Features of Vision Transformer for Face Anti-Spoofing—0
FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models—0
Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing—0
Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images—0
Generative Classifier for Domain Generalization—0
Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing—0
Enhancing Learnable Descriptive Convolutional Vision Transformer for Face Anti-SpoofingCode0
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