  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.
