进行时间一致性检查,以检测LIDAR对自主车辆感知进行攻击 (Temporal Consistency Checks to Detect LiDAR Spoofing Attacks on Autonomous Vehicle Perception)

LiDAR sensors are used widely in Autonomous Vehicles for better perceiving the environment which enables safer driving decisions. Recent work has demonstrated serious LiDAR spoofing attacks with alarming consequences. In particular, model-level LiDAR spoofing attacks aim to inject fake depth measurements to elicit ghost objects that are erroneously detected by 3D Object Detectors, resulting in hazardous driving decisions. In this work, we explore the use of motion as a physical invariant of genuine objects for detecting such attacks. Based on this, we propose a general methodology, 3D Temporal Consistency Check (3D-TC2), which leverages spatio-temporal information from motion prediction to verify objects detected by 3D Object Detectors. Our preliminary design and implementation of a 3D-TC2 prototype demonstrates very promising performance, providing more than 98% attack detection rate with a recall of 91% for detecting spoofed Vehicle (Car) objects, and is able to achieve real-time detection at 41Hz

翻译：“激光雷达”传感器在自主车辆中广泛使用,以便更好地了解环境,从而作出更安全的驾驶决定。最近的工作显示,“激光雷达”系统严重地掩盖攻击,其后果令人震惊。特别是,“激光雷达”模型级攻击旨在注入假深度测量,以探测3D物体探测器误测到的鬼物体,从而导致危险驾驶决定。在这项工作中,我们探索如何使用运动作为真实物体的物理变异物来探测这种攻击。在此基础上,我们提出了一个一般方法,即3DAR时间一致性检查(3D-TC2),利用运动预测的瞬时信息来核查3D物体探测器探测到的物体。我们初步设计和实施了3D-TC2模型,显示了非常有希望的性能,提供了98%以上的攻击探测率,回顾了91%用于探测潜射车辆物体,并且能够在41Hz实现实时探测。