论文标题
基于深度学习的车道安全系统在物理世界对抗攻击下的安全性
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack
论文作者
论文摘要
巷道维护援助系统（LKAS）很方便且广泛可用，但同时也非常安全和安全。在这项工作中，我们设计并实施了攻击基于DNN的LKASE的第一种系统方法。我们将肮脏的道路贴片视为一种新颖和特定领域的威胁模型，以实用和隐身性。我们将攻击作为优化问题制定，并解决连续摄像机框架攻击之间相互依存的挑战。我们在最先进的LKA上评估了我们的方法，我们的初步结果表明，我们的攻击可以成功地导致其在短短1.3秒内驱逐车道边界。
Lane-Keeping Assistance System (LKAS) is convenient and widely available today, but also extremely security and safety critical. In this work, we design and implement the first systematic approach to attack real-world DNN-based LKASes. We identify dirty road patches as a novel and domain-specific threat model for practicality and stealthiness. We formulate the attack as an optimization problem, and address the challenge from the inter-dependencies among attacks on consecutive camera frames. We evaluate our approach on a state-of-the-art LKAS and our preliminary results show that our attack can successfully cause it to drive off lane boundaries within as short as 1.3 seconds.
