Towards Robust Physical-world Backdoor Attacks on Lane Detection

arXiv:2405.05553v1 Announce Type: new
Abstract: 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 …

abstract arxiv attack methods attacks autonomous autonomous driving autonomous driving systems backdoor control cruise cs.ai cs.cv deep learning detection driving dynamic however lane detection robust role systems type vulnerable world