Laser-Based LiDAR Spoofing: Effects Validation, Capability Quantification, and Countermeasures

Abstract

Autonomous vehicles (AVs) and robots increasingly exploit light detection and ranging (LiDAR)-based 3-D object detection systems to detect obstacles in the environment. Correct detection and classification are important to ensure safe driving. Although previous work has demonstrated the feasibility of manipulating point clouds to spoof 3-D object detectors, most of these attempts are performed digitally. In this article, we investigate the possibility of physically fooling LiDAR-based 3-D object detection by injecting adversarial point clouds using lasers. First, we develop a laser transceiver that can inject up to 4200 points, and can measure the scanning cycle of victim LiDARs to schedule the spoofing laser signals. By designing a control signal method that converts the coordinates of point clouds to control signals and an adversarial point cloud optimization method with physical constraints of LiDARs and attack capabilities, we manage to inject spoofing point cloud with desired point cloud shapes into the victim LiDAR physically. We can launch four types of attacks, i.e., naive hiding, record-based creating, optimization-based hiding, and optimization-based creating. Extensive experiments demonstrate the effectiveness of our attacks against two commercial LiDAR and three detectors. We further analyze the impact of our attacks on four fusion-based detectors. This article concludes with experiments on defense methods and discussion on potential defense strategies at both the sensor and AV system levels.