Towards Stealthy and Effective Backdoor Attacks on Lane Detection: A Naturalistic Data Poisoning Approach

Understand

Deep learning-based lane detection (LD) plays a critical role in autonomous driving and advanced driver assistance systems.

• However, its vulnerability to backdoor attacks presents a significant security concern.
• Existing backdoor attack methods on LD often exhibit limited practical utility due to the artificial and conspicuous nature of their triggers.
• To address this limitation and investigate the impact of more ecologically valid backdoor attacks on LD models, we examine the common data poisoning attack and introduce DBALD, a novel diffusion-based data poisoning framework for generating naturalistic backdoor triggers.