Adversarial Attacks on Traffic Sign Recognition: A Survey

Traffic sign recognition is an essential component of perception in autonomous vehicles, which is currently performed almost exclusively with deep neural networks (DNNs). However, DNNs are known to be vulnerable to adversarial attacks. Several previous works have demonstrated the feasibility of adve...

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Bibliographic Details
Published in2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) pp. 1 - 6
Main Authors Pavlitska, Svetlana, Lambing, Nico, Zollner, J. Marius
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.07.2023
Subjects
adversarial attacks
Artificial neural networks
Autonomous vehicles
Computational modeling
image classification
Mechatronics
object detection
Surveys
traffic sign recognition
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Summary:Traffic sign recognition is an essential component of perception in autonomous vehicles, which is currently performed almost exclusively with deep neural networks (DNNs). However, DNNs are known to be vulnerable to adversarial attacks. Several previous works have demonstrated the feasibility of adversarial attacks on traffic sign recognition models. Traffic signs are particularly promising for adversarial attack research due to the ease of performing real-world attacks using printed signs or stickers. In this work, we survey existing works performing either digital or real-world attacks on traffic sign detection and classification models. We provide an overview of the latest advancements and highlight the existing research areas that require further investigation.
DOI:10.1109/ICECCME57830.2023.10252727