Backdoor Defense in Transportation Cyber-Physical Systems Using Frequency Domain Hybrid Distillation

In the context of transportation cyber-physical systems (T-CPS), backdoor attacks leveraging traffic images have emerged as a significant security threat. As T-CPS increasingly relies on visual information, such as real-time images captured by traffic cameras, for tasks like traffic sign recognition and autonomous driving, the risk of image-based backdoor attacks has grown substantially. Although various detection-based defense techniques have shown some success in identifying backdoored models, they often fail to fully eliminate backdoor effects, leaving residual security risks. To address this challenge, we propose a Frequency-Domain Hybrid Distillation (FDHD) method for backdoor defense, which effectively weakens the association between backdoor triggers and target labels by combining distillation mechanisms in both the frequency and pixel domains. Furthermore, we design a loss function that integrates feature reconstruction with adaptive alignment, enhancing the student network’s ability to mimic the teacher network and thereby bolstering the backdoor defense capability. Extensive experiments conducted by FDHD on multiple benchmark datasets against the five latest attacks demonstrate that our proposed defense method effectively reduces backdoor threats while maintaining high accuracy in predicting clean samples. This approach will protect against image-based backdoor attacks in T-CPS and lay the foundation for enhancing future traffic safety.