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
Deep Residual Learning for Image Recognition
tensorflow/models
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CVPR 2016
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Very Deep Convolutional Networks for Large-Scale Image Recognition
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
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.
Learning Generalized Spoof Cues for Face Anti-spoofing
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.
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
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.
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
clks-wzz/FAS-SGTD
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CVPR 2020
Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing.
Learn Convolutional Neural Network for Face Anti-Spoofing
Moreover, the nets trained using combined data from two datasets have less biases between two datasets.
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
omggggg/mmdg
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Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against presentation attacks.
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
SoftwareGift/FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019
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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.
Improving Face Anti-Spoofing by 3D Virtual Synthesis
sicxu/Deep3DFaceRecon_pytorch
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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.
