Stealthy Physical Backdoor Attacks Against Traffic Sign Recognition Systems

Wenbo Jiang, Hongwei Li, Yuxin Lu, Shuai Yuan, Rui Zhang, Zihan Wang, Qiyang Song, Dongxiao Liu

Abstract: Recent advancements in deep learning have led to remarkable progress in autonomous driving technology, with deep neural network (DNN)-based traffic sign recognition systems (TSRS) playing a crucial role. However, recent studies indicate that TSRS are vulnerable to backdoor attacks, where the backdoor TSRS behaves normally on clean traffic signs but consistently misclassifies backdoor-triggered traffic signs into a designated target class. Notably, while backdoor attacks in the digital domain are effective, their effectiveness may diminish in the physical world due to quality degradation during image transmission. Existing physical backdoor attacks typically rely on specific stickers or transformations as backdoor triggers, which are not stealthy and natural enough in the physical world. To address these limitations, we propose two stealthy physical backdoor attacks against DNN-based TSRS from two different perspectives. On the one hand, we utilize the natural phenomenon of chipped paints on traffic signs as the backdoor trigger. Specifically, we develop an automatic traffic sign segmentation algorithm to identify the edges of the target sign and simulate chipped paint to create poisoned samples. On the other hand, instead of manipulating the target traffic sign, we use the specific filter lens (attached to the in-vehicle camera) as the backdoor trigger, where the parameters of the filter lens are optimized by the Genetic Algorithm (GA). Extensive experiments conducted on the GTSRB and TSRD datasets demonstrate the effectiveness of our proposed backdoor attacks in both digital and physical environments.

External IDs:dblp:conf/icc/JiangLLYZWSL25