Face Anti-Spoofing

28 papers with code • 5 benchmarks • 12 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 )

Greatest papers with code

A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing

SoftwareGift/FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019 CVPR 2019

To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities.

Face Anti-Spoofing Face Recognition

Dual-Cross Central Difference Network for Face Anti-Spoofing

ZitongYu/CDCN 4 May 2021

In this paper, we propose two Cross Central Difference Convolutions (C-CDC), which exploit the difference of the center and surround sparse local features from the horizontal/vertical and diagonal directions, respectively.

Face Anti-Spoofing Face Recognition

Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

ZitongYu/CDCN 17 Apr 2020

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks.

Face Anti-Spoofing Face Recognition

Searching Central Difference Convolutional Networks for Face Anti-Spoofing

ZitongYu/CDCN CVPR 2020

Here we propose a novel frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information.

Face Anti-Spoofing Face Recognition +1

CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results

Davidzhangyuanhan/CelebA-Spoof 25 Feb 2021

It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects.

Face Anti-Spoofing

CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations

Davidzhangyuanhan/CelebA-Spoof ECCV 2020

The main reason is that current face anti-spoofing datasets are limited in both quantity and diversity.

Face Anti-Spoofing

Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing

clks-wzz/FAS-SGTD CVPR 2020

Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing.

Face Anti-Spoofing Face Recognition

Learning Generalized Spoof Cues for Face Anti-spoofing

vis-var/lgsc-for-fas 8 May 2020

In this paper, we reformulate FAS in an anomaly detection perspective and propose a residual-learning framework to learn the discriminative live-spoof differences which are defined as the spoof cues.

Anomaly Detection Face Anti-Spoofing