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Title: WIP: Practical Removal Attacks on LiDAR-based Object Detection in Autonomous Driving
LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous driving (AD). Meanwhile, such a safety-critical application strongly motivates its security research. A recent line of research demonstrates that one can manipulate the LiDAR point cloud and fool object detection by firing malicious lasers against LiDAR. However, these efforts evaluate only a specific LiDAR (VLP-16) and do not consider the state-of-the-art defense mechanisms in the recent LiDARs, so-called next-generation LiDARs. In this WIP work, we report our recent progress in the security analysis of the next-generation LiDARs. We identify a new type of LiDAR spoofing attack applicable to a much more general and recent set of LiDARs. We find that our attack can remove >72% of points in a 10×10 m2 area and can remove real vehicles in the physical world. We also discuss our future plans.  more » « less
Award ID(s):
2145493 1932464 1929771
NSF-PAR ID:
10427123
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
ISOC Symposium on Vehicle Security and Privacy (VehicleSec)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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