Physical Backdoor Trigger Activation of Autonomous Vehicle Using Reachability Analysis

Wenqing Li; Yue Wang; Muhammad Shafique; Saif Eddin Jabari

doi:10.1109/CDC49753.2023.10383622

Physical Backdoor Trigger Activation of Autonomous Vehicle Using Reachability Analysis

Wenqing Li, Yue Wang, Muhammad Shafique, Saif Eddin Jabari

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Recent studies reveal that Autonomous Vehicles (AVs) can be manipulated by hidden backdoors, causing them to perform harmful actions when activated by physical triggers. However, it is still unclear how these triggers can be activated while adhering to traffic principles. Understanding this vulnerability in a dynamic traffic environment is crucial. This work addresses this gap by presenting physical trigger activation as a reachability problem of controlled dynamic system. Our technique identifies security-critical areas in traffic systems where trigger conditions for accidents can be reached, and provides intended trajectories for how those conditions can be reached. Testing on typical traffic scenarios showed the system can be successfully driven to trigger conditions with near 100% activation rate. Our method benefits from identifying AV vulnerability and enabling effective safety strategies.

Original language	English (US)
Title of host publication	2023 62nd IEEE Conference on Decision and Control, CDC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	821-826
Number of pages	6
ISBN (Electronic)	9798350301243
DOIs	https://doi.org/10.1109/CDC49753.2023.10383622
State	Published - 2023
Event	62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: Dec 13 2023 → Dec 15 2023

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	62nd IEEE Conference on Decision and Control, CDC 2023
Country/Territory	Singapore
City	Singapore
Period	12/13/23 → 12/15/23

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/CDC49753.2023.10383622

Cite this

Li, W., Wang, Y., Shafique, M., & Jabari, S. E. (2023). Physical Backdoor Trigger Activation of Autonomous Vehicle Using Reachability Analysis. In 2023 62nd IEEE Conference on Decision and Control, CDC 2023 (pp. 821-826). (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC49753.2023.10383622

Physical Backdoor Trigger Activation of Autonomous Vehicle Using Reachability Analysis. / Li, Wenqing; Wang, Yue; Shafique, Muhammad et al.
2023 62nd IEEE Conference on Decision and Control, CDC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 821-826 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, W, Wang, Y, Shafique, M & Jabari, SE 2023, Physical Backdoor Trigger Activation of Autonomous Vehicle Using Reachability Analysis. in 2023 62nd IEEE Conference on Decision and Control, CDC 2023. Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 821-826, 62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, 12/13/23. https://doi.org/10.1109/CDC49753.2023.10383622

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title = "Physical Backdoor Trigger Activation of Autonomous Vehicle Using Reachability Analysis",

abstract = "Recent studies reveal that Autonomous Vehicles (AVs) can be manipulated by hidden backdoors, causing them to perform harmful actions when activated by physical triggers. However, it is still unclear how these triggers can be activated while adhering to traffic principles. Understanding this vulnerability in a dynamic traffic environment is crucial. This work addresses this gap by presenting physical trigger activation as a reachability problem of controlled dynamic system. Our technique identifies security-critical areas in traffic systems where trigger conditions for accidents can be reached, and provides intended trajectories for how those conditions can be reached. Testing on typical traffic scenarios showed the system can be successfully driven to trigger conditions with near 100% activation rate. Our method benefits from identifying AV vulnerability and enabling effective safety strategies.",

author = "Wenqing Li and Yue Wang and Muhammad Shafique and Jabari, {Saif Eddin}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 62nd IEEE Conference on Decision and Control, CDC 2023 ; Conference date: 13-12-2023 Through 15-12-2023",

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doi = "10.1109/CDC49753.2023.10383622",

language = "English (US)",

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AB - Recent studies reveal that Autonomous Vehicles (AVs) can be manipulated by hidden backdoors, causing them to perform harmful actions when activated by physical triggers. However, it is still unclear how these triggers can be activated while adhering to traffic principles. Understanding this vulnerability in a dynamic traffic environment is crucial. This work addresses this gap by presenting physical trigger activation as a reachability problem of controlled dynamic system. Our technique identifies security-critical areas in traffic systems where trigger conditions for accidents can be reached, and provides intended trajectories for how those conditions can be reached. Testing on typical traffic scenarios showed the system can be successfully driven to trigger conditions with near 100% activation rate. Our method benefits from identifying AV vulnerability and enabling effective safety strategies.

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Physical Backdoor Trigger Activation of Autonomous Vehicle Using Reachability Analysis

Abstract

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ASJC Scopus subject areas

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