Phantom: Physical Object Interactions as Dynamic Triggers for NMS-Exploited Backdoors

Tianlin Huo, Dongchuan Ran, Ranjie Duan, Yao Zhu, Peilun Du, Ningbo Yao, Huanqian Yan, Xu Han, Qiang Yun, Yuzheng Tan, Yang Bao, Yuan He; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 27906-27915

Backdoor attacks pose potential threats to object detection models, highlighting the importance of studying their security. However, existing backdoor attacks mainly rely on trigger-specific intrinsic features, which limits their practicality in real-world scenarios. In this paper, we propose a novel backdoor attack that leverages dynamic object interactions in realistic scenarios to activate malicious behavior. By hijacking the Non-Maximum Suppression (NMS) process in object detectors, this attack demonstrates robust effectiveness, including misclassification, mislocalization, and object appearance/disappearance, while maintaining the model's normal performance on clean inputs. Experimental results demonstrate that our attack exhibits significant attack performance across various object detectors and datasets, and remains effective both in physical environments and under existing defense mechanisms. These findings highlight the urgent need to develop efficient and robust defense strategies against backdoor attacks.