Detection and Recovery of Adversarial Slow-Pose Drift in Offloaded Visual-Inertial Odometry

• URL: http://arxiv.org/abs/2509.07130v1
• Date: Mon, 08 Sep 2025 18:31:40 GMT
• Title: Detection and Recovery of Adversarial Slow-Pose Drift in Offloaded Visual-Inertial Odometry
• Authors: Soruya Saha, Md Nurul Absurd, Saptarshi Debroy,
• Abstract summary: Current trend of offloading VIO to edge servers can lead server-side threat surface.<n>We present an unsupervised, label-free detection and recovery mechanism.<n>We evaluate the approach in a realistic offloaded-VIO environment using ILLIXR testbed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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