Detecting Perception-Based Attacks using Visual Odometry: Inconsistency Modeling and Checking on Robotic States

Yuan Xu, Gelei Deng, Tianwei Zhang

Abstract: Perception systems in robotic vehicles are crucial for safe and efficient operation, providing key state estimates necessary for planning and control. However, these systems are increasingly vulnerable to perception-based attacks, such as odometry spoofing, position spoofing, obstacle hiding, and object misclassification, which can lead to catastrophic failures. In this paper, we propose a novel approach to detect perception-based attacks by modeling inconsistencies between the physical and estimated states of the robot. Our approach offers a unified methodology for detecting different types of attacks with high accuracy and minimal computational overhead. We validate our method through extensive simulations and real-world scenarios, achieving a 99.5% success rate in detecting attacks, while maintaining a low latency (within 100ms).

External IDs:dblp:conf/icra/XuDZ25