SOTAVerified

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 )

Papers

Showing 5160 of 204 papers

TitleStatusHype
Deep Pixel-wise Binary Supervision for Face Presentation Attack DetectionCode0
Deep Anomaly Detection for Generalized Face Anti-SpoofingCode0
Latent Distribution Adjusting for Face Anti-SpoofingCode0
Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural NetworksCode0
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-SpoofingCode0
A visualization method for data domain changes in CNN networks and the optimization method for selecting thresholds in classification tasksCode0
AdvFAS: A robust face anti-spoofing framework against adversarial examplesCode0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
Deep Ensemble Learning with Frame Skipping for Face Anti-SpoofingCode0
Generalized Presentation Attack Detection: a face anti-spoofing evaluation proposalCode0
Show:102550
← PrevPage 6 of 21Next →

No leaderboard results yet.