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research-article

Malicious Attacks against Multi-Sensor Fusion in Autonomous Driving

Authors:

Chunming QiaoAuthors Info & Claims

ACM MobiCom '24: Proceedings of the 30th Annual International Conference on Mobile Computing and Networking

Pages 436 - 451

https://doi.org/10.1145/3636534.3649372

Published: 29 May 2024 Publication History

Abstract

Multi-sensor fusion has been widely used by autonomous vehicles (AVs) to integrate the perception results from different sensing modalities including LiDAR, camera and radar. Despite the rapid development of multi-sensor fusion systems in autonomous driving, their vulnerability to malicious attacks have not been well studied. Although some prior works have studied the attacks against the perception systems of AVs, they only consider a single sensing modality or a camera-LiDAR fusion system, which can not attack the sensor fusion system based on LiDAR, camera, and radar. To fill this research gap, in this paper, we present the first study on the vulnerability of multi-sensor fusion systems that employ LiDAR, camera, and radar. Specifically, we propose a novel attack method that can simultaneously attack all three types of sensing modalities using a single type of adversarial object. The adversarial object can be easily fabricated at low cost, and the proposed attack can be easily performed with high stealthiness and flexibility in practice. Extensive experiments based on a real-world AV testbed show that the proposed attack can continuously hide a target vehicle from the perception system of a victim AV using only two small adversarial objects.

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Cited By

Hu LZhang JZhang JCheng SWang YZhang WYu N(2024)Security analysis and adaptive false data injection against multi-sensor fusion localization for autonomous drivingInformation Fusion10.1016/j.inffus.2024.102822(102822)Online publication date: Nov-2024
https://doi.org/10.1016/j.inffus.2024.102822

Index Terms

Malicious Attacks against Multi-Sensor Fusion in Autonomous Driving
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Security and privacy
  1. Software and application security
    1. Domain-specific security and privacy architectures

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ACM MobiCom '24: Proceedings of the 30th Annual International Conference on Mobile Computing and Networking

December 2024

2476 pages

ISBN:9798400704895

DOI:10.1145/3636534

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Weisong Shi,
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Cited By

Hu LZhang JZhang JCheng SWang YZhang WYu N(2024)Security analysis and adaptive false data injection against multi-sensor fusion localization for autonomous drivingInformation Fusion10.1016/j.inffus.2024.102822(102822)Online publication date: Nov-2024
https://doi.org/10.1016/j.inffus.2024.102822

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