Towards Real-Time Defense against Object-Based LiDAR Attacks in Autonomous Driving

LiDAR (Light Detection and Ranging)-based object detection is a cornerstone of autonomous vehicle perception systems. Modern LiDAR perception relies heavily on deep neural networks (DNNs), which enable accurate object detection by learning geometric features from 3D point clouds. However, recent studies have shown that these systems are vulnerable to object-based adversarial attacks, where physical adversarial objects are strategically placed in the environment to manipulate LiDAR point clouds and mislead detection models. These attacks are practical, stealthy, and require no specialized hardware, posing a serious threat to the safety and reliability of AVs. Despite these risks, existing defense methods suffer from significant limitations, including high computational overhead, limited generalizability and effectiveness, and the inability to operate in real time. In this paper, we propose the first real-time defense mechanism against object-based LiDAR attacks in autonomous driving. Our solution is both detection model-agnostic and attack-agnostic, requiring no prior knowledge of the number, shape, size, or placement of adversarial objects. Positioned between the sensing and perception modules of the AV pipeline, the defense processes LiDAR point clouds in real time and employs a novel generative model that enables efficient and effective identification and removal of adversarial points from suspicious regions. Extensive experiments in both simulated and real-world environments demonstrate that our approach achieves high attack detection rates with minimal latency. This work offers a practical and robust defense solution to a growing security threat in autonomous driving.