Unified Detection of Digital and Physical Face Attacks
Debayan Deb, Xiaoming Liu, Anil K. Jain

TL;DR
This paper introduces UniFAD, a unified face attack detection framework that effectively clusters and detects 25 attack types across digital, physical, and adversarial categories, outperforming existing methods.
Contribution
UniFAD is a novel multi-task learning approach that automatically clusters attack types, improving generalization across attack categories and achieving high detection accuracy.
Findings
Achieves 94.73% TDR at 0.2% FDR on large dataset
Detects attacks within 3 milliseconds
Identifies attack types and categories with high accuracy
Abstract
State-of-the-art defense mechanisms against face attacks achieve near perfect accuracies within one of three attack categories, namely adversarial, digital manipulation, or physical spoofs, however, they fail to generalize well when tested across all three categories. Poor generalization can be attributed to learning incoherent attacks jointly. To overcome this shortcoming, we propose a unified attack detection framework, namely UniFAD, that can automatically cluster 25 coherent attack types belonging to the three categories. Using a multi-task learning framework along with k-means clustering, UniFAD learns joint representations for coherent attacks, while uncorrelated attack types are learned separately. Proposed UniFAD outperforms prevailing defense methods and their fusion with an overall TDR = 94.73% @ 0.2% FDR on a large fake face dataset consisting of 341K bona fide images and…
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Videos
Unified Detection of Digital and Physical Face Attacks· youtube
Taxonomy
TopicsDigital Media Forensic Detection · Rabies epidemiology and control · Biometric Identification and Security
