Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures

By Jiachen Sun, Yulong Cao, Qi Alfred Chen, Z. Morley Mao. USENIX Security Symposium, 2020

314 citations recorded by the source corpus.

Perception plays a pivotal role in autonomous driving systems, which utilizes onboard sensors like cameras and LiDARs (Light Detection and Ranging) to assess surroundings. Recent studies have demonstrated that LiDAR-based perception is vulnerable to spoofing attacks, in which adversaries spoof a fake vehicle in front of a victim self-driving car by strategically transmitting laser signals to the victim's LiDAR sensor. However, existing attacks suffer from effectiveness and generality limitations. In this work, we perform the first study to explore the general vulnerability of current LiDAR-based perception architectures and discover that the ignored occlusion patterns in LiDAR point clouds make self-driving cars vulnerable to spoofing attacks. We construct the first black-box spoofing attack based on our identified vulnerability, which universally achieves around 80% mean success rates on all target models. We perform the first defense study, proposing CARLO to mitigate LiDAR spoofing attacks. CARLO detects spoofed data by treating ignored occlusion patterns as invariant physical features, which reduces the mean attack success rate to 5.5%. Meanwhile, we take the first step towards exploring a general architecture for robust LiDAR-based perception, and propose SVF that embeds the neglected physical features into end-to-end learning. SVF further reduces the mean attack success rate to around 2.3%.

View Paper: https://www.semanticscholar.org/paper/9d13c6b39f7ea80b5b91dff29f5e682ed1436893

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yulong Cao
2. Chaowei Xiao
3. Ningfei Wang
4. Bo Li
5. Qi Alfred Chen
6. Ruigang Yang
7. Dawei Yang
8. Jin Fang

Citations in the corpus

• Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks (2021, methodology): “To systematically generate adversarial 3D objects, we adopt an optimization-based approach similar to prior works [5]– [11].”