Attribution-Based Scanline Perturbation Attack on 3d Detectors of Lidar Point Clouds

Abstract

LiDAR point cloud data is widely utilized in autonomous driving systems and has significantly improved the 3D detection performance with well-designed deep neural network models. However, due to the complexity of real-world environments and model vulnerability, false detections or malicious attacks may cause severe accidents in unseen situations. In this paper, we propose a novel attack approach, Attribution-based Scanline Perturbation (ASP), an efficient and physically possible adversarial attack method for 3D detectors. ASP first utilizes attribution methods to identify critical points for the detection model and perturbs them along the laser beams by simulating the situation in which particles exist between the LiDAR sensor and objects, which can actually occur in snow or sandstorm weather. Extensive experiments on practical 3D detectors validate the effectiveness of our approach in misleading the model and causing both false and missed detections.