Too Good to Be Safe: Tricking Lane Detection in Autonomous Driving with Crafted Perturbations
Too Good to Be Safe: Tricking Lane Detection in Autonomous Driving with Crafted Perturbations
复制标题
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Pengfei Jing;Qiyi Tang;Yue Du;Lei Xue;Xiapu Luo;Ting Wang;Sen Nie;Shi Wu
中科院分区：
文献类型：
--
作者：
Pengfei Jing;Qiyi Tang;Yue Du;Lei Xue;Xiapu Luo;Ting Wang;Sen Nie;Shi Wu
自动驾驶正在迅速发展，并通过采用机器学习算法自动完成各种任务而取得了可喜的性能。车道检测是主要任务之一，因为其结果直接影响转向决策。尽管最近的研究发现了自动驾驶汽车的一些漏洞，但据我们所知，还没有研究过真实车辆中车道检测模块的安全性。在本文中，我们对真实车辆中的车道检测模块进行了首次研究，发现目标模块的过度敏感可以被利用来对车辆发起攻击。更准确地说，过于敏感的车道检测模块可能会将对手引入的路面上的小标记视为有效车道，然后将车辆驶向错误的方向。设计这样小的道路标记是具有挑战性的，这些标记应该被车道检测模块感知，但驾驶员不会注意到。手动操纵道路标记来对车道检测模块发起攻击是非常耗时且容易出错的。我们提出了一种新颖的两阶段方法，在解决了多项技术挑战后自动确定此类道路标记。我们的方法首先决定相机图像上的最佳扰动，然后将它们映射到物理世界中的道路标记。我们在特斯拉 Model S 车辆上进行了大量实验，实验结果表明，车道检测模块可以通过非常不显眼的扰动来欺骗以创建车道，从而在自动转向模式下误导车辆。
Autonomous driving is developing rapidly and has achieved promising performance by adopting machine learning algorithms to finish various tasks automatically. Lane detection is one of the major tasks because its result directly affects the steering decisions. Although recent studies have discovered some vulnerabilities in autonomous vehicles, to the best of our knowledge, none has investigated the security of lane detection module in real vehicles. In this paper, we conduct the first investigation on the lane detection module in a real vehicle, and reveal that the over-sensitivity of the target module can be exploited to launch attacks on the vehicle. More precisely, an over-sensitive lane detection module may regard small markings on the road surface, which are introduced by an adversary, as a valid lane and then drive the vehicle in the wrong direction. It is challenging to design such small road markings that should be perceived by the lane detection module but unnoticeable to the driver. Manual manipulation of the road markings to launch attacks on the lane detection module is very labor-intensive and error-prone. We propose a novel two-stage approach to automatically determine such road markings after tackling several technical challenges. Our approach first decides the optimal perturbations on the camera image and then maps them to road markings in physical world. We conduct extensive experiments on a Tesla Model S vehicle, and the experimental results show that the lane detection module can be deceived by very unobtrusive perturbations to create a lane, thus misleading the vehicle in auto-steer mode.