Paper Info
Title
Poster: Towards Large-Scale Measurement Study on LiDAR Spoofing Attacks against Object Detection
Abstract
ABSTRACTLiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing of the surrounding environment. The recent rapid deployment of autonomous driving (AD) has highly benefited from the advancement of LiDARs. At the same time, the safety-critical application strongly motivates its security research. Recent studies demonstrate that they can manipulate the LiDAR point cloud and fool object detection by shooting malicious lasers against LiDAR scanning. However, prior efforts focus on limited types of LiDARs and object detection models, and their threat models are not clearly validated in the real world. To fill the critical research gap, we plan to conduct the first large-scale measurement study on LiDAR spoofing attacks against a wide variety of LiDARs with major object detectors. To perform this measurement, we first significantly improved the LiDAR spoofing capability (30x more spoofing points than the prior attack) with more careful optics and functional electronics, which allows us to be the first to clearly demonstrate and quantify key attack capabilities assumed in prior works. In this poster, we present our preliminary results on VLP-16 and our research plan.
Year
DOI
Venue
2022
10.1145/3548606.3563537
Computer and Communications Security
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Takami Sato101.01
Yuki Hayakawa211.07
Ryo Suzuki3227.58
Yohsuke Shiiki402.37
Kentaro Yoshioka5549.04
Qi Chen626124.99