Face Anti-Spoofing

19 papers with code · Computer Vision

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

Greatest papers with code

Improving Face Anti-Spoofing by 3D Virtual Synthesis

2 Jan 2019cleardusk/3DDFA

Specifically, we consider a printed photo as a flat surface and mesh it into a 3D object, which is then randomly bent and rotated in 3D space.

FACE ANTI-SPOOFING FACE RECOGNITION

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

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

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

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

17 Apr 2020ZitongYu/CDCN

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

CVPR 2020 ZitongYu/CDCN

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 NEURAL ARCHITECTURE SEARCH

Learning Generalized Spoof Cues for Face Anti-spoofing

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

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

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

24 Jul 2020Davidzhangyuanhan/CelebA-Spoof

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

CVPR 2020 clks-wzz/FAS-SGTD

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

FACE ANTI-SPOOFING FACE RECOGNITION

Regularized Fine-grained Meta Face Anti-spoofing

25 Nov 2019rshaojimmy/AAAI2020-RFMetaFAS

Besides, to further enhance the generalization ability of our model, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration.

DOMAIN GENERALIZATION FACE ANTI-SPOOFING FACE RECOGNITION META-LEARNING