Face Anti-Spoofing

39 papers with code • 5 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 )

Most implemented papers

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.

Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection

Saiyam26/Face-Anti-Spoofing-using-DeePixBiS 9 Jul 2019

The proposed approach achieves an HTER of 0% in Replay Mobile dataset and an ACER of 0. 42% in Protocol-1 of OULU dataset outperforming state of the art methods.

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.

Learn Convolutional Neural Network for Face Anti-Spoofing

mnikitin/Learn-Convolutional-Neural-Network-for-Face-Anti-Spoofing 24 Aug 2014

Moreover, the nets trained using combined data from two datasets have less biases between two datasets.

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.

Deep Learning for Face Anti-Spoofing: A Survey

ZitongYu/DeepFAS 28 Jun 2021

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs).

face anti-spoofing based on color texture analysis

coderwangson/Face-anti-spoofing-based-on-color-texture-analysis 19 Nov 2015

Research on face spoofing detection has mainly been focused on analyzing the luminance of the face images, hence discarding the chrominance information which can be useful for discriminating fake faces from genuine ones.

Face De-Spoofing: Anti-Spoofing via Noise Modeling

yaojieliu/ECCV2018-FaceDeSpoofing ECCV 2018

In this work, motivated by the noise modeling and denoising algorithms, we identify a new problem of face de-spoofing, for the purpose of anti-spoofing: inversely decomposing a spoof face into a spoof noise and a live face, and then utilizing the spoof noise for classification.

Exploiting temporal and depth information for multi-frame face anti-spoofing

laoshiwei/face-anti-spoofing 13 Nov 2018

Face anti-spoofing is significant to the security of face recognition systems.