ControlLoc: Physical-World Hijacking Attack on Camera-based Perception in Autonomous Driving
Chen Ma, Ningfei Wang, Zhengyu Zhao, Qian Wang, Qi Alfred Chen, Chao Shen
Abstract
Recent research shows that adversarial patches can attack object detectors in camera-based perception for Autonomous Driving (AD). However, camera-based perception includes more than object detection; it also involves Multiple Object Tracking (MOT), which enhances robustness by requiring consistent detection across multiple frames before affecting tracking and thus, driving decisions. This makes attacks on object detection alone less effective. To attack such robust systems, a digital hijacking attack has been proposed, aiming to induce dangerous scenarios such as collisions. However, this attack has limited effectiveness, especially in the physical world.
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