Imperial College London
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DrSoterisDemetriou

Faculty of Engineering, Department of Computing

Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 8237s.demetriou Website CV

 
 
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Assistant

 

Ms Lucy Atthis +44 (0)20 7594 8259

 
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Location

 

353ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Hau:2022:10.1109/SPW54247.2022.9833890,
author = {Hau, Z and Demetriou, S and Lupu, EC},
doi = {10.1109/SPW54247.2022.9833890},
pages = {229--235},
publisher = {IEEE},
title = {Using 3D shadows to detect object hiding attacks on autonomous vehicle perception},
url = {http://dx.doi.org/10.1109/SPW54247.2022.9833890},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Autonomous Vehicles (AVs) are mostly reliant on LiDAR sensors which enable spatial perception of their surroundings and help make driving decisions. Recent works demonstrated attacks that aim to hide objects from AV perception, which can result in severe consequences. 3D shadows, are regions void of measurements in 3D point clouds which arise from occlusions of objects in a scene. 3D shadows were proposed as a physical invariant valuable for detecting spoofed or fake objects. In this work, we leverage 3D shadows to locate obstacles that are hidden from object detectors. We achieve this by searching for void regions and locating the obstacles that cause these shadows. Our proposed methodology can be used to detect an object that has been hidden by an adversary as these objects, while hidden from 3D object detectors, still induce shadow artifacts in 3D point clouds, which we use for obstacle detection. We show that using 3D shadows for obstacle detection can achieve high accuracy in matching shadows to their object and provide precise prediction of an obstacle’s distance from the ego-vehicle.
AU  - Hau,Z
AU  - Demetriou,S
AU  - Lupu,EC
DO  - 10.1109/SPW54247.2022.9833890
EP  - 235
PB  - IEEE
PY  - 2022///
SN  - 2639-7862
SP  - 229
TI  - Using 3D shadows to detect object hiding attacks on autonomous vehicle perception
UR  - http://dx.doi.org/10.1109/SPW54247.2022.9833890
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000853036900023&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/9833890
UR  - http://hdl.handle.net/10044/1/107814
ER  -
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