Name
Title | ||
|---|---|---|
Too Good to Be Safe: Tricking Lane Detection in Autonomous Driving with Crafted Perturbations |
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2021 | PROCEEDINGS OF THE 30TH USENIX SECURITY SYMPOSIUM | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengfei Jing | 1 | 0 | 0.34 |
| Qiyi Tang | 2 | 1 | 1.70 |
| Yuefeng Du | 3 | 5 | 5.50 |
| Lei Xue | 4 | 103 | 16.03 |
| Xiapu Luo | 5 | 1302 | 110.23 |
| Ting Wang | 6 | 664 | 65.43 |
| Sen Nie | 7 | 4 | 1.30 |
| Shi Wu | 8 | 21 | 2.71 |