Face Anti-Spoofing
66 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:
-
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 )
Benchmarks
These leaderboards are used to track progress in Face Anti-Spoofing
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
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
Latest papers
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.
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.
Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the CVPR2023 workshop.
Instance-Aware Domain Generalization for Face Anti-Spoofing
To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels.
Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video
wenzhengzeng/mpeblink
•
•
CVPR 2023
Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video.
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations.
M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System
chenqikong/m3fas
•
•
Face presentation attacks (FPA), also known as face spoofing, have brought increasing concerns to the public through various malicious applications, such as financial fraud and privacy leakage.
Cyclically Disentangled Feature Translation for Face Anti-spoofing
We further extend CDFTN for multi-target domain adaptation by leveraging data from more unlabeled target domains.
Multi-domain Learning for Updating Face Anti-spoofing Models
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.
