SUEDE:Shared Unified Experts for Physical-Digital Face Attack Detection Enhancement

• URL: http://arxiv.org/abs/2504.04818v1
• Date: Mon, 07 Apr 2025 08:17:54 GMT
• Title: SUEDE:Shared Unified Experts for Physical-Digital Face Attack Detection Enhancement
• Authors: Zuying Xie, Changtao Miao, Ajian Liu, Jiabao Guo, Feng Li, Dan Guo, Yunfeng Diao,
• Abstract summary: Face recognition systems are vulnerable to physical attacks and digital threats.<n>The inherent differences among various attack types present significant challenges in identifying a common feature space.<n>We propose SUEDE, the Shared Unified Experts for Physical-Digital Face Attack Detection Enhancement.
• Score: 19.140558657697866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face recognition systems are vulnerable to physical attacks (e.g., printed photos) and digital threats (e.g., DeepFake), which are currently being studied as independent visual tasks, such as Face Anti-Spoofing and Forgery Detection. The inherent differences among various attack types present significant challenges in identifying a common feature space, making it difficult to develop a unified framework for detecting data from both attack modalities simultaneously. Inspired by the efficacy of Mixture-of-Experts (MoE) in learning across diverse domains, we explore utilizing multiple experts to learn the distinct features of various attack types. However, the feature distributions of physical and digital attacks overlap and differ. This suggests that relying solely on distinct experts to learn the unique features of each attack type may overlook shared knowledge between them. To address these issues, we propose SUEDE, the Shared Unified Experts for Physical-Digital Face Attack Detection Enhancement. SUEDE combines a shared expert (always activated) to capture common features for both attack types and multiple routed experts (selectively activated) for specific attack types. Further, we integrate CLIP as the base network to ensure the shared expert benefits from prior visual knowledge and align visual-text representations in a unified space. Extensive results demonstrate SUEDE achieves superior performance compared to state-of-the-art unified detection methods.

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