BibTex format
@inproceedings{Hau:2021,
author = {Hau, Z and Co, KT and Demetriou, S and Lupu, EC},
title = {Object Removal Attacks on LiDAR-based 3D Object Detectors},
url = {http://arxiv.org/abs/2102.03722v1},
year = {2021}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - CPAPER
AB - LiDARs play a critical role in Autonomous Vehicles' (AVs) perception andtheir safe operations. Recent works have demonstrated that it is possible tospoof LiDAR return signals to elicit fake objects. In this work we demonstratehow the same physical capabilities can be used to mount a new, even moredangerous class of attacks, namely Object Removal Attacks (ORAs). ORAs aim toforce 3D object detectors to fail. We leverage the default setting of LiDARsthat record a single return signal per direction to perturb point clouds in theregion of interest (RoI) of 3D objects. By injecting illegitimate points behindthe target object, we effectively shift points away from the target objects'RoIs. Our initial results using a simple random point selection strategy showthat the attack is effective in degrading the performance of commonly used 3Dobject detection models.
AU - Hau,Z
AU - Co,KT
AU - Demetriou,S
AU - Lupu,EC
PY - 2021///
TI - Object Removal Attacks on LiDAR-based 3D Object Detectors
UR - http://arxiv.org/abs/2102.03722v1
UR - http://dx.doi.org/10.14722/autosec.2021.23
ER -