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Title: WIP: Towards the Practicality of the Adversarial Attack on Object Tracking in Autonomous Driving
Recently, adversarial examples against object detection have been widely studied. However, it is difficult for these attacks to have an impact on visual perception in autonomous driving because the complete visual pipeline of real-world autonomous driving systems includes not only object detection but also object tracking. In this paper, we present a novel tracker hijacking attack against the multi-target tracking algorithm employed by real-world autonomous driving systems, which controls the bounding box of object detection to spoof the multiple object tracking process. Our approach exploits the detection box generation process of the anchor-based object detection algorithm and designs new optimization methods to generate adversarial patches that can successfully perform tracker hijacking attacks, causing security risks. The evaluation results show that our approach has 85% attack success rate on two detection models employed by real-world autonomous driving systems. We discuss our potential next step for this work.  more » « less
Award ID(s):
2145493 1932464 1929771
NSF-PAR ID:
10427129
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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