TY - GEN
T1 - Laser Shield
T2 - 61st ACM/IEEE Design Automation Conference, DAC 2024
AU - Zhang, Qingjie
AU - Chi, Lijun
AU - Wang, Di
AU - Msahli, Mounira
AU - Memmi, Gerard
AU - Zhang, Tianwei
AU - Zhang, Chao
AU - Qiu, Han
N1 - Publisher Copyright:
© 2024 Copyright is held by the owner/author(s).
PY - 2024/11/7
Y1 - 2024/11/7
N2 - 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.
AB - 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.
U2 - 10.1145/3649329.3657358
DO - 10.1145/3649329.3657358
M3 - Conference contribution
AN - SCOPUS:85211175816
T3 - Proceedings - Design Automation Conference
BT - Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 June 2024 through 27 June 2024
ER -