Face Anti-Spoofing

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

Libraries

Datasets

Subtasks

Most implemented papers

Deep Residual Learning for Image Recognition

Content

Very Deep Convolutional Networks for Large-Scale Image Recognition

Learning Generalized Spoof Cues for Face Anti-spoofing

Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection

Searching Central Difference Convolutional Networks for Face Anti-Spoofing

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

Learn Convolutional Neural Network for Face Anti-Spoofing

Improving Face Anti-Spoofing by 3D Virtual Synthesis

A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing

FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-Spoofing