LiDAttack: Robust Black-Box Attack on LiDAR-Based Object Detection

With the rise of autonomous driving, LiDAR-based object detection using deep neural networks (DNNs) has shown exceptional performance. However, DNNs are vulnerable to adversarial attacks, especially on LiDAR sensors. Existing attacks face challenges in terms of (i) effective -they're generally effective in white-box scenarios, but suffer significant performance degradation in black-box scenarios; (ii) robustness -they're much less effective when exposed to the diverse changes in the real world (i.e., angle and distance changes); (iii) concealing -most attacks overlook the crucial stealth aspect, making them susceptible to detection and defense. To address these issues, LiDAttack is introduced as a robust black-box attack that leverages genetic algorithms with simulated annealing to precisely control perturbation points, ensuring both stealth and efficacy. Extensive experiments on KITTI, nuScenes, and a custom dataset demonstrate LiDAttack's high attack success rate (ASR) of up to 90% across diverse detection models (PointRCNN, PointPillar, PV-RCNN++). Practical robustness is validated in real world tests, both indoors and outdoors. For concealment, the generated adversarial object volume is restricted to under 0.1% of the target object's volume, achieving ASR up to 90%. The code is available at https://github.com/Cinderyl/LiDAttack.git