AD-SRF: Attack-Defense Semantic Reinforcement for Multi-Modal Image Fusion
Existing image fusion methods struggle to balance visual quality and semantic attributes due to the inherent conflict between their optimization objectives. To address this challenge, we propose an attack-defense semantic reinforcement framework for multi-modal image fusion, termed AD-SRF. By decoupling the optimization of these two aspects and then losslessly reintegrating them, this framework injects rich semantic attributes into the fused image without compromising its visual quality. First, we develop a transformer-based restorative visual fusion network as a strong visual baseline. Equipped with the cross-modal retrieval module and the information restoration-fusion collaborative los...
| Authors | Hao Zhang, Zhiqian Zheng, Linfeng Tang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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