Face Anti-Spoofing
65 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 )
Benchmarks
These leaderboards are used to track progress in Face Anti-Spoofing
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Libraries
Use these libraries to find Face Anti-Spoofing models and implementationsDatasets
Replay-Attack
MSU-MFSD
CASIA-MFSD
SiW
CASIA-FASD
CelebA-Spoof
CASIA-SURF
Replay-Mobile
HiFiMask
HQ-WMCA
Most implemented papers
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
SoftwareGift/FeatheNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019
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Face Anti-spoofing gains increased attentions recently in both academic and industrial fields.
Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
The proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks.
Deep Learning for Face Anti-Spoofing: A Survey
Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs).
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition
doantienthongbku/Implementation-patchnet
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CVPR 2022
Previous works leverage auxiliary pixel-level supervision and domain generalization approaches to address unseen spoof types.
Generalizable Method for Face Anti-Spoofing with Semi-Supervised Learning
Face anti-spoofing has drawn a lot of attention due to the high security requirements in biometric authentication systems.
Multi-domain Learning for Updating Face Anti-spoofing Models
chelsea234/multi-domain-learning-fas
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In this work, we study multi-domain learning for face anti-spoofing(MD-FAS), where a pre-trained FAS model needs to be updated to perform equally well on both source and target domains while only using target domain data for updating.
Latent Distribution Adjusting for Face Anti-Spoofing
In this work, we propose a unified framework called Latent Distribution Adjusting (LDA) with properties of latent, discriminative, adaptive, generic to improve the robustness of the FAS model by adjusting complex data distribution with multiple prototypes.
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.
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.
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.
