Face Anti-Spoofing
64 papers with code • 8 benchmarks • 17 datasets
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:
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Print attack: The attacker uses someone’s photo. The image is printed or displayed on a digital device.
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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.
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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 )
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Latest papers
Gradient Alignment for Cross-Domain Face Anti-Spoofing
Recent advancements in domain generalization (DG) for face anti-spoofing (FAS) have garnered considerable attention.
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against presentation attacks.
SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models
For the face forgery detection task, we evaluate GAN-based and diffusion-based data with both visual and acoustic modalities.
Cross-Database Liveness Detection: Insights from Comparative Biometric Analysis
In an era where biometric security serves as a keystone of modern identity verification systems, ensuring the authenticity of these biometric samples is paramount.
Domain-Generalized Face Anti-Spoofing with Unknown Attacks
Although face anti-spoofing (FAS) methods have achieved remarkable performance on specific domains or attack types, few studies have focused on the simultaneous presence of domain changes and unknown attacks, which is closer to real application scenarios.
FLIP: Cross-domain Face Anti-spoofing with Language Guidance
Specifically, we show that aligning the image representation with an ensemble of class descriptions (based on natural language semantics) improves FAS generalizability in low-data regimes.
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces.
Enhancing Mobile Face Anti-Spoofing: A Robust Framework for Diverse Attack Types under Screen Flash
In this paper, we propose an attack type robust face anti-spoofing framework under light flash, called ATR-FAS.
Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
Face presentation attacks (PA), also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems.
Joint Statistical and Causal Feature Modulated Face Anti-Spoofing
In this paper, we propose a hierarchical feature modulation (HFM) approach for stable face anti-spoofing in unseen domains and unseen attacks.