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

arXiv: https://arxiv.org/abs/2509.07130 · HTML: https://mlsanity.com/paper/2509.07130/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Distributed Certifiably Correct Pose-Graph Optimization](https://mlsanity.com/paper/1911.03721/) (2019)
- [SoK: Rethinking Sensor Spoofing Attacks against Robotic Vehicles from a Systematic View](https://mlsanity.com/paper/2205.04662/) (2022)

## Built on by (most cited first, 3 of 3)

- [Detection of Misreporting Attacks on Software-Defined Immersive Environments](https://mlsanity.com/paper/2509.18040/) (2025)
- [Understanding Sensor Vulnerabilities in Industrial XR Tracking](https://mlsanity.com/paper/2602.14413/) (2026)
- [Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines](https://mlsanity.com/paper/2603.16823/) (2026)

## Abstract (arXiv)

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.