TY - GEN
T1 - Poster
T2 - 28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022
AU - Sato, Takami
AU - Hayakawa, Yuki
AU - Suzuki, Ryo
AU - Shiiki, Yohsuke
AU - Yoshioka, Kentaro
AU - Chen, Qi Alfred
N1 - Funding Information:
This research was supported in part by the NSF CNS-1932464, CNS-1929771, CNS-2145493, USDOT UTC Grant 69A3552047138, JST CREST JPMJCR21D2, JSPS KAKENHI 21K20413, and JST SPRING JPMJSP2123.
Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/11/7
Y1 - 2022/11/7
N2 - LiDAR (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.
AB - LiDAR (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.
KW - 3D object detection
KW - Autonomous driving
KW - Lidar
KW - Spoofing attack
UR - http://www.scopus.com/inward/record.url?scp=85143082490&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143082490&partnerID=8YFLogxK
U2 - 10.1145/3548606.3563537
DO - 10.1145/3548606.3563537
M3 - Conference contribution
AN - SCOPUS:85143082490
T3 - Proceedings of the ACM Conference on Computer and Communications Security
SP - 3459
EP - 3461
BT - CCS 2022 - Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
PB - Association for Computing Machinery
Y2 - 7 November 2022 through 11 November 2022
ER -