Affiliation
University of ArizonaIssue Date
2022-11-07Keywords
adversarial machine learningautonomous driving
neural networks
object tracking
video surveillance
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Association for Computing MachineryCitation
Muller, R., Man, Y., Celik, Z. B., Li, M., & Gerdes, R. (2022, November). Physical hijacking attacks against object trackers. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (pp. 2309-2322).Rights
© 2022 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License (http://creativecommons.org/licenses/by-nc-nd/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Modern autonomous systems rely on both object detection and object tracking in their visual perception pipelines. Although many recent works have attacked the object detection component of autonomous vehicles, these attacks do not work on full pipelines that integrate object tracking to enhance the object detector's accuracy. Meanwhile, existing attacks against object tracking either lack real-world applicability or do not work against a powerful class of object trackers, Siamese trackers. In this paper, we present AttrackZone, a new physically-realizable tracker hijacking attack against Siamese trackers that systematically determines valid regions in an environment that can be used for physical perturbations. AttrackZone exploits the heatmap generation process of Siamese Region Proposal Networks in order to take control of an object's bounding box, resulting in physical consequences including vehicle collisions and masked intrusion of pedestrians into unauthorized areas. Evaluations in both the digital and physical domain show that AttrackZone achieves its attack goals 92% of the time, requiring only 0.3-3 seconds on average. © 2022 Owner/Author.Note
Open access articleISSN
1543-7221ISBN
9781450394505Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1145/3548606.3559390
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Except where otherwise noted, this item's license is described as © 2022 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License (http://creativecommons.org/licenses/by-nc-nd/4.0/).