A2RNet: Adversarial Attack Resilient Network for Robust Infrared and Visible Image Fusion

作     者：Li, Jiawei Yu, Hongwei Chen, Jiansheng Ding, Xinlong Wang, Jinlong Liu, Jinyuan Zou, Bochao Ma, Huimin 

作者机构：School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Software Technology Dalian University of Technology Dalian China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Image fusion 

摘      要：Infrared and visible image fusion (IVIF) is a crucial technique for enhancing visual performance by integrating unique information from different modalities into one fused image. Exiting methods pay more attention to conducting fusion with undisturbed data, while overlooking the impact of deliberate interference on the effectiveness of fusion results. To investigate the robustness of fusion models, in this paper, we propose a novel adversarial attack resilient network, called A2RNet. Specifically, we develop an adversarial paradigm with an anti-attack loss function to implement adversarial attacks and training. It is constructed based on the intrinsic nature of IVIF and provide a robust foundation for future research advancements. We adopt a Unet as the pipeline with a transformer-based defensive refinement module (DRM) under this paradigm, which guarantees fused image quality in a robust coarse-to-fine manner. Compared to previous works, our method mitigates the adverse effects of adversarial perturbations, consistently maintaining high-fidelity fusion results. Furthermore, the performance of downstream tasks can also be well maintained under adversarial attacks. Copyright © 2024, The Authors. All rights reserved.