发现并修复边缘计算中视觉惯性定位的隐蔽姿态漂移攻击
Detection and Recovery of Adversarial Slow-Pose Drift in Offloaded Visual-Inertial Odometry
- 通过学习正常运动时序规律，无监督检测异常姿态偏差
- 在多种欺骗强度下显著降低轨迹与姿态误差
- 适合部署于远程虚拟现实系统的安全防护
视觉-惯性里程计（VIO）通过融合摄像头与惯性测量单元（IMU）数据实现实时位姿估计，支持沉浸式虚拟现实（VR）。然而，当前将VIO任务卸载至边缘服务器的趋势，使服务端面临潜在威胁：细微的姿态伪造可累积成显著漂移，且不易被启发式检测发现。本文研究该威胁，提出一种无需标签、无监督的检测与恢复机制。模型在无攻击的会话数据上训练，学习运动时序规律，以识别运行时偏离并触发恢复，恢复位姿一致性。我们在真实环境下的离线VIO系统中，利用ILLIXR测试平台，在多种欺骗强度下评估该方法。实验结果表明，相较于无防御基线，该方法在常用性能指标上显著降低了轨迹误差和姿态误差。
原文摘要 · Abstract (English)
Visual-Inertial Odometry (VIO) supports immersive Virtual Reality (VR) by fusing camera and Inertial Measurement Unit (IMU) data for real-time pose. However, current trend of offloading VIO to edge servers can lead server-side threat surface where subtle pose spoofing can accumulate into substantial drift, while evading heuristic checks. In this paper, we study this threat and present an unsupervised, label-free detection and recovery mechanism. The proposed model is trained on attack-free sessions to learn temporal regularities of motion to detect runtime deviations and initiate recovery to restore pose consistency. We evaluate the approach in a realistic offloaded-VIO environment using ILLIXR testbed across multiple spoofing intensities. Experimental results in terms of well-known performance metrics show substantial reductions in trajectory and pose error compared to a no-defense baseline.
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