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.

Adversa has published comprehensive research on the security and trustworthiness of AI systems worldwide during the last decade. The report reveals the most critical real-world security threats facing AI and effective countermeasures to protect these systems. The research considers the impact of ongoing regulations concerning AI security in the EU and USA. "Building trust in the security and safety of machine learning is crucial. We are asking people to put their faith in what is essentially a black box, and for the AI revolution to succeed, we must build trust. We won't have many chances at getting it right. The risks are too high – but so are the benefits," said Oliver Rochford, Adversa Advisor.