Secure Object Detection of Autonomous Vehicles Against Adversarial Attacks

This paper addresses the critical challenge of reliable object detection in autonomous vehicles operating in dynamic urban environments, particularly when facing adversarial attacks on perception systems. It presents a novel dualvalidation methodology leveraging the synergy of image based and point cloud based object detection systems. The approach comprises sensor calibration to align data from both sources, an attack detection algorithm utilizing cross-validation technique to identify inconsistencies, and a self-restoration strategy to ensure correct detection despite malicious manipulation. The methodology has been tested in a complex urban environment with adversarial scenarios including patch and random removal attacks. Experimental results demonstrate the robustness and accuracy of the proposed method in maintaining reliable object detection under adversarial conditions.