Laser Shield: a Physical Defense with Polarizer against Laser Attacks on Autonomous Driving Systems
Authors: 
Qingjie Zhang, Lijun Chi, Di Wang, Mounira Msahli, Gerard Memmi, Tianwei Zhang, Chao Zhang, Han QiuAuthors Info & Claims
Qingjie Zhang, Lijun Chi, Di Wang, Mounira Msahli, Gerard Memmi, Tianwei Zhang, Chao Zhang, Han QiuAuthors Info & ClaimsArticle No.: 65, Pages 1 - 6
Abstract
Autonomous driving systems (ADS) are boosted with deep neural networks (DNN) to perceive environments, while their security is doubted by DNN's vulnerability to adversarial attacks. Among them, a diversity of laser attacks emerges to be a new threat due to its minimal requirements and high attack success rate in the physical world. Nevertheless, current defense methods exhibit either a low defense success rate or a high computation cost against laser attacks. To fill this gap, we propose Laser Shield which leverages a polarizer along with a min-energy rotation mechanism to eliminate adversarial lasers from ADS scenes. We also provide a physical world dataset, LAPA, to evaluate its performance. Through exhaustive experiments with three baselines, four metrics, and three settings, Laser Shield is proved to surpass SOTA performance.
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Published: 07 November 2024
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