Deep learning-based lane detection (LD) plays a critical role in autonomous driving systems, such as adaptive cruise control. However, it is vulnerable to backdoor attacks. Existing backdoor attack methods on LD exhibit limited effectiveness in dynamic real-world scenarios, primarily because they fail to consider dynamic scene factors, including changes in driving perspectives (e.g., viewpoint transformations) and environmental conditions (e.g., weather or lighting changes). To tackle this issue, this paper introduces BadLANE, a dynamic scene adaptation backdoor attack for LD designed to withstand changes in real-world dynamic scene factors. To address the challenges posed by changing driving perspectives, we propose an amorphous trigger pattern composed of shapeless pixels. This trigger design allows the backdoor to be activated by various forms or shapes of mud spots or pollution on the road or lens, enabling adaptation to changes in vehicle observation viewpoints during driving. To mitigate the effects of environmental changes, we design a meta-learning framework to train meta-generators tailored to different environmental conditions. These generators produce meta-triggers that incorporate diverse environmental information, such as weather or lighting conditions, as the initialization of the trigger patterns for backdoor implantation, thus enabling adaptation to dynamic environments. Extensive experiments on various commonly used LD models in both digital and physical domains validate the effectiveness of our attacks, outperforming other baselines significantly (+25.15% on average in Attack Success Rate). Our codes will be available upon paper publication.

翻译：基于深度学习的车道检测在自动驾驶系统中扮演着关键角色，例如自适应巡航控制。然而，它容易受到后门攻击。现有的车道检测后门攻击方法在动态真实世界场景中效果有限，主要原因是未能考虑动态场景因素，包括驾驶视角的变化（例如，视点变换）和环境条件的变化（例如，天气或光照变化）。为了解决这一问题，本文提出了BadLANE，一种针对车道检测的动态场景自适应后门攻击，旨在抵御真实世界动态场景因素的变化。为了应对驾驶视角变化带来的挑战，我们提出了一种由无定形像素组成的非晶态触发模式。这种触发设计使得后门能够被道路或镜头上的各种形式或形状的泥点或污渍激活，从而适应驾驶过程中车辆观察视角的变化。为了减轻环境变化的影响，我们设计了一个元学习框架来训练针对不同环境条件定制的元生成器。这些生成器产生的元触发器融入了多样化的环境信息，例如天气或光照条件，作为后门植入触发模式的初始化，从而实现动态环境的适应。在数字和物理领域对各种常用车道检测模型进行的大量实验验证了我们攻击的有效性，显著优于其他基线方法（攻击成功率平均提升+25.15%）。我们的代码将在论文发表后提供。