An Online Defense against Object-based LiDAR Attacks in Autonomous Driving

LiDAR (Light Detection and Ranging) has been widely used in autonomous driving to perceive the surrounding environment of self-driving cars. Advanced LiDAR perception systems typically leverage deep neural networks (DNNs) to achieve high performance. However, the vulnerability of DNNs to malicious attacks provides attackers with the means to compromise the LiDAR perception system, potentially causing traffic accidents. Recently, object-based attacks against LiDAR perception systems have drawn significant attention. In such attacks, the attacker can easily fool the LiDAR perception system by placing physical objects within the driving environment. Despite the practicality of these attacks and their potential catastrophic consequences in autonomous driving, there is currently no effective and practical defense against them. To address this issue, we propose a novel online defense mechanism against object-based LiDAR attacks. This mechanism operates in an online manner, aiming to identify and remove the adversarial LiDAR points generated by the objects used by attackers before the data is fed into the perception module of autonomous driving systems. It is not only effective and efficient for real-world autonomous driving but also attack-agnostic and capable of identifying adversarial objects used by attackers. Extensive experiments in both simulated environments and real-world scenarios using a LiDAR perception testbed demonstrate the effectiveness and practicability of the proposed defense.