毫米波雷达穿墙窃取虚拟现实用户隐私，精度超90%。
mmSpyVR: Exploiting mmWave Radar for Penetrating Obstacles to Uncover Privacy Vulnerability of Virtual Reality
- 用迁移学习从毫米波信号中提取VR特征
- 穿墙识别应用达98.5%，输入密码准确率92.6%
- 揭露新隐私漏洞，适合安全与VR研究者关注
虚拟现实（VR）虽提升体验，却带来显著隐私风险。本文揭示一种新型漏洞：攻击者可利用毫米波（mmWave）信号穿透障碍物，在无需物理接触或连接设备的情况下窃取VR用户隐私。我们提出mmSpyVR框架，包含两部分：(i) 基于迁移学习的特征提取模型，从毫米波信号中提取VR特征；(ii) 基于注意力机制的隐私窃取模块，从提取特征中推断隐私信息。该系统成功从穿透障碍物的毫米波信号中还原关键隐私数据。通过经伦理审查的用户实验，22名参与者在三个厂商的四类实验场景中，系统实现98.5%的应用识别准确率和92.6%的按键输入识别准确率。此发现对网络安全、隐私保护及VR技术发展具有深远影响。我们已与VR厂商Meta沟通探讨缓解策略。数据与代码公开于https://github.com/luoyumei1-a/mmSpyVR/
原文摘要 · Abstract (English)
Virtual reality (VR), while enhancing user experiences, introduces significant privacy risks. This paper reveals a novel vulnerability in VR systems that allows attackers to capture VR privacy through obstacles utilizing millimeter-wave (mmWave) signals without physical intrusion and virtual connection with the VR devices. We propose mmSpyVR, a novel attack on VR user's privacy via mmWave radar. The mmSpyVR framework encompasses two main parts: (i) A transfer learning-based feature extraction model to achieve VR feature extraction from mmWave signal. (ii) An attention-based VR privacy spying module to spy VR privacy information from the extracted feature. The mmSpyVR demonstrates the capability to extract critical VR privacy from the mmWave signals that have penetrated through obstacles. We evaluate mmSpyVR through IRB-approved user studies. Across 22 participants engaged in four experimental scenes utilizing VR devices from three different manufacturers, our system achieves an application recognition accuracy of 98.5\% and keystroke recognition accuracy of 92.6\%. This newly discovered vulnerability has implications across various domains, such as cybersecurity, privacy protection, and VR technology development. We also engage with VR manufacturer Meta to discuss and explore potential mitigation strategies. Data and code are publicly available for scrutiny and research at https://github.com/luoyumei1-a/mmSpyVR/
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