UiAs: User-Independent 3D Facial Anti-Spoofing via Multi-modal Wireless Signals

2026-08-29

AI Summary

该研究提出UiAs系统，利用毫米波和声波两种无线信号解决3D面部反欺骗问题，通过跨模态减法消除用户依赖性几何变化的影响。

Abstract

Face authentication is widely deployed in security-sensitive applications, while increasingly realistic 3D spoofing attacks pose growing threats. High-fidelity 3D masks can reproduce facial appearance and geometry but cannot replicate the intrinsic physical responses of living tissue, which can be actively probed by wireless signals. However, the resulting liveness cues captured by wireless signals are entangled with user-dependent facial geometry, limiting cross-user generalization. We present UiAs, a multimodal user-independent 3D facial anti-spoofing system using electromagnetic (mmWave) and mechanical (acoustic) waves. The two modalities share similar user-dependent geometric variations, allowing UiAs to suppress them through cross-modal subtraction while preserving modality-specific liveness cues. Their complementary physical responses further improve live/spoof discrimination. In practical deployments, multiple materials (e.g., skin, hair, eyeglasses, or face coverings) may also bias liveness representations, while spoofing materials are diverse and open-ended. UiAs addresses both through skin-anchored contrastive learning. We evaluate UiAs with real 3D spoofing attacks, which achieves 93.25\% accuracy for unseen users without user-specific physical-signal enrollment.

Problem

• 3D Facial Anti-Spoofing
• Wireless Signals
• User-Independent
• Multi-modal
• Liveness Cues

Innovation

• multimodal
• user-independent
• cross-modal subtraction
• skin-anchored contrastive learning
• liveness cues