UiAs：基于多模态无线信号的用户无关三维人脸反欺骗

中文摘要 AI 辅助

人脸认证广泛应用于对安全性要求极高的场景，而日益逼真的三维欺骗攻击带来了越来越大的威胁。高保真三维面具可复现人脸外观和几何结构，但无法复制活体组织的固有物理响应，这类响应可通过无线信号主动探测。不过，无线信号捕获的活体线索与用户相关的人脸几何结构纠缠在一起，限制了跨用户泛化能力。我们提出UiAs，一种使用电磁（毫米波）和机械（声学）波的多模态用户无关三维人脸反欺骗系统。两种模态共享相似的用户相关几何变化，使UiAs可通过跨模态减法抑制这些变化，同时保留模态特有的活体线索。它们互补的物理响应进一步提升了活体/欺骗区分能力。在实际部署中，多种材料（如皮肤、头发、眼镜或面部遮盖物）也可能偏置活体表示，而欺骗材料多样且开放。UiAs通过皮肤锚定对比学习解决这两个问题。我们使用真实三维欺骗攻击评估UiAs，在无需用户特定物理信号注册的情况下，对未见过的用户实现了93.25%的准确率。

英文摘要

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.

发表机构

• University of Electronic Science and Technology of China（电子科技大学）
• Shanghai University of Finance and Economics（上海财经大学）
• Shanghai Jiao Tong University（上海交通大学）

机构由 AI 辅助整理，请以论文原文为准。