Physical Hijacking Attacks against Object Trackers

Raymond Muller, Yanmao Man, Z. Berkay Celik, Ming Li, Ryan Gerdes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationCCS 2022 - Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages2309-2322
Number of pages14
ISBN (Electronic)9781450394505
DOIs
StatePublished - Nov 7 2022
Event28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022 - Los Angeles, United States
Duration: Nov 7 2022Nov 11 2022

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Conference

Conference28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022
Country/TerritoryUnited States
CityLos Angeles
Period11/7/2211/11/22

Keywords

  • adversarial machine learning
  • autonomous driving
  • neural networks
  • object tracking
  • video surveillance

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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