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FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection by Li, Yongze; Li, Ning; Liu, Ajian; Ma, Hui; Yang, Liying; Chen, Xihong; Liang, Zhiyao; Liang, Yanyan; Wan, Jun; Lei, Zhen is a scholarly article available to read on EtoBox.

What is FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection about?

Facial recognition systems are vulnerable to physical (e.g., printed photos) and digital (e.g., DeepFake) face attacks. Existing methods struggle to simultaneously detect physical and digital attacks due to: 1) significant intra-class variations between these attack types, and 2) the inadequacy of spatial information alone to comprehensively capture live and fake cues. To address these issues, we propose a unified attack detection model termed Frequency-Aware and Attack-Agnostic CLIP (FA\textsuperscript{3}-CLIP), which introduces attack-agnostic prompt learning to express generic live and fake cues derived from the fusion of spatial and frequency features, enabling unified detection of live faces and all categories of attacks. Specifically, the attack-agnostic prompt module generates generic live and fake prompts within the language branch to extract corresponding generic representations from both live and fake faces, guiding the model to learn a unified feature space for unified attack detection. Meanwhile, the module adaptively generates the live/fake conditional bias from the original spatial and frequency information to optimize the generic prompts accordingly, reducing the imp

Author

Li, Yongze; Li, Ning; Liu, Ajian; Ma, Hui; Yang, Liying; Chen, Xihong; Liang, Zhiyao; Liang, Yanyan; Wan, Jun; Lei, Zhen

Published

2025

Language

EN