Robust Roadside Physical Adversarial Attack Against Deep Learning in Lidar Perception Modules

As Autonomous Vehicles (AVs) mature into viable transportation solutions, mitigating potential vehicle control security risks becomes increasingly important. Perception modules in AVs combine multiple sensors to perceive the surrounding environment. As such, they have been the focus of efforts to exploit the aforementioned risks due to their critical role in controlling autonomous driving technology. Despite extensive and thorough research into the vulnerability of camera-based sensors, vulnerabilities originating from Lidar sensors and their corresponding deep learning models in AVs remain comparatively untouched. Being aware that small roadside objects can be occasionally incorrectly identified as vehicles through on-board deep learning models, we propose a novel adversarial attack inspired by this phenomenon in both white-box and black-box scenarios. The adversarial attacks proposed in this paper are launched against deep learning models that perform object detection tasks through raw 3D points collected by a Lidar sensor in an autonomous driving scenario. In comparison to existing works, our attack creates not only adversarial point clouds in simulated environments, but also robust adversarial objects that can cause behavioral reactions in state of the art autonomous driving systems. Defense methods are then proposed and evaluated against this type of adversarial objects.