Stealthy and Effective Physical Adversarial Attacks in Autonomous Driving-Reference-Cited by-同舟云学术

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Stealthy and Effective Physical Adversarial Attacks in Autonomous Driving

Published:2024 Issue: Volume:19 Page:6795-6809
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Zhou Man¹^ORCID,Zhou Wenyu¹^ORCID,Huang Jie¹,Yang Junhui¹,Du Minxin²,Li Qi³^ORCID

Affiliation:

1. Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

2. Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

3. Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

NSFC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/10206/10319981/10583962.pdf?arnumber=10583962

Reference55 articles.

1. Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems

2. SLAP: Improving physical adversarial examples with short-lived adversarial perturbations;Lovisotto

3. Dirty road can attack: Security of deep learning based automated lane centering under physical-world attack;Sato

4. Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon

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