[2603.25170] Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.25170 (cs) [Submitted on 26 Mar 2026] Title:Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling Authors:Shiji Zhao, Shukun Xiong, Maoxun Yuan, Yao Huang, Ranjie Duan, Qing Guo, Jiansheng Chen, Haibin Duan, Xingxing Wei View a PDF of the paper titled Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling, by Shiji Zhao and 8 other authors View PDF HTML (experimental) Abstract:In complex environments, infrared object detection exhibits broad applicability and stability across diverse scenarios. However, infrared object detection is vulnerable to both common corruptions and adversarial examples, leading to potential security risks. To improve the robustness of infrared object detection, current methods mostly adopt a data-driven ideology, which only superficially drives the network to fit the training data without specifically considering the unique characteristics of infrared images, resulting in limited robustness. In this paper, we revisit infrared physical knowledge and find that relative thermal radiation relations between different classes can be regarded as a reliable knowledge source under the complex scenarios of adversarial examples and common corruptions. Thus, we theoretically model thermal radiation relations based on the rank order of gray values for different classes, and further quantify the stability ...