UiAs: User-Independent 3D Facial Anti-Spoofing via Multi-modal Wireless Signals
Cryptography and Security
Summary
The authors developed a system called UiAs to prevent fake 3D face masks from tricking face recognition. UiAs uses both wireless signals (mmWave) and sound waves to detect if a face is real by checking natural physical reactions that fake masks can’t copy. By comparing these two types of signals, the system removes differences caused by individual face shapes, focusing on signs of life instead. This approach works well even with new users or different materials like hair or glasses and achieved over 93% accuracy without needing to know the user’s specific data beforehand.
face authentication3D spoofingmmWave signalsacoustic wavesliveness detectioncross-modal subtractioncontrastive learningbiometricsuser-independent spoof detectionfacial geometry
Authors
Zhiwei chen, Lebin Lyu, Yimo Zhang, Dingyu Zhong, Yijie Li, Yichao Chen, Dian Ding, Jiguo Yu, Xiaosong Zhang, Yongzhao Zhang
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.