@inproceedings{
yin2024backdoor,
title={Backdoor Attacks on Bimodal Salient Object Detection with {RGB}-Thermal Data},
author={Wen Yin and Bin Benjamin Zhu and Yulai Xie and Pan Zhou and Dan Feng},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=fBeeQlkIM8}
}
RGB-Thermal salient object detection (RGBT-SOD) plays a critical role in complex scene recognition fields such as autonomous driving, yet security research in this area remains limited. This paper introduces the first backdoor attack targeting RGBT-SOD, generating saliency maps on triggered inputs that depict non-existent salient objects chosen by the attacker, or designate no salient region (all black pixels) or the entire image as a salient region (all white pixels). We uncover that triggers possess an influence range for generating non-existent salient objects, supported by a theoretical approximation provided in this study. Extensive experimental evaluations validate the efficacy of our attack in both digital domain and physical-world scenarios. Notably, our dual-modality backdoor attack achieves an Attack Success Rate (ASR) of 86.72% with only 5 pairs of images in model training. Despite exploring potential countermeasures, we find them ineffective in thwarting our attacks, underscoring the urgent need for robust defenses against sophisticated backdoor attacks in RGBT-SOD systems.
Backdoor Attacks on Bimodal Salient Object Detection with RGB-Thermal Data
Wen Yin Bin Benjamin Zhu Yulai Xie Pan Zhou Dan Feng