Localization-Aware Adversarial Attacks Against LiDAR-Based 3D Object Detection

Abstract

Robustness in 3D object detection is paramount for safety-critical applications such as autonomous driving. However, existing adversarial attack methodologies often fail to fully leverage the intrinsic spatial geometric characteristics of 3D point clouds. To address this limitation, we propose a novel differentiable method named Localization-Aware Adversarial Attacks (LAA). LAA explicitly incorporates the absolute coordinates of bounding boxes, the relative spatial relationships with ground truth objects, and rotation angles into its adversarial optimization objective. By generating imperceptible point cloud perturbations, LAA aims to directly disrupt the localization awareness of 3D object detectors. Extensive experiments on the KITTI dataset against a variety of mainstream 3D object detectors demonstrate that LAA exhibits remarkable effectiveness in compromising the detectors’ localization capabilities. This research reveals the vulnerability of current 3D object detectors regarding localization awareness and provides valuable insights for constructing more robust detection systems.