# PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation

arXiv: https://arxiv.org/abs/2308.03979 · HTML: https://mlsanity.com/paper/2308.03979/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 55 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 30 of 37)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://mlsanity.com/paper/1904.02884/) (2019)
- [Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser](https://mlsanity.com/paper/1712.02976/) (2017)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [Feature Denoising for Improving Adversarial Robustness](https://mlsanity.com/paper/1812.03411/) (2018)
- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [DenseFuse: A Fusion Approach to Infrared and Visible Images](https://mlsanity.com/paper/1804.08361/) (2018)
- [Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond](https://mlsanity.com/paper/2101.11517/) (2021)
- [Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning](https://mlsanity.com/paper/2003.02437/) (2020)
- [Towards Robust Vision Transformer](https://mlsanity.com/paper/2105.07926/) (2021)
- [Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models](https://mlsanity.com/paper/1808.01688/) (2018)
- [When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks](https://mlsanity.com/paper/1911.10695/) (2019)
- [Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration](https://mlsanity.com/paper/2205.11876/) (2022)
- [DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs](https://mlsanity.com/paper/1712.07384/) (2017)
- [SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness](https://mlsanity.com/paper/2207.12391/) (2022)
- [DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2003.09210/) (2020)
- [Image Super-Resolution as a Defense Against Adversarial Attacks](https://mlsanity.com/paper/1901.01677/) (2019)
- [Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond](https://mlsanity.com/paper/2110.00455/) (2021)
- [Fast End-to-End Trainable Guided Filter](https://mlsanity.com/paper/1803.05619/) (2018)
- [Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond](https://mlsanity.com/paper/2203.16931/) (2022)
- [A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion](https://mlsanity.com/paper/2305.15862/) (2023)
- [Edge-aware Guidance Fusion Network for RGB Thermal Scene Parsing](https://mlsanity.com/paper/2112.05144/) (2021)
- [RGB-T Semantic Segmentation with Location, Activation, and Sharpening](https://mlsanity.com/paper/2210.14530/) (2022)
- [Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation](https://mlsanity.com/paper/2003.06555/) (2020)
- [An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection](https://mlsanity.com/paper/2305.09999/) (2023)

## Built on by (most cited first, 26 of 26)

- [Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation](https://mlsanity.com/paper/2404.04256/) (2024)
- [Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption](https://mlsanity.com/paper/2501.10761/) (2025)
- [Task-driven Image Fusion with Learnable Fusion Loss](https://mlsanity.com/paper/2412.03240/) (2024)
- [All-weather Multi-Modality Image Fusion: Unified Framework and 100k Benchmark](https://mlsanity.com/paper/2402.02090/) (2024)
- [Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion](https://mlsanity.com/paper/2309.01113/) (2023)
- [UMCFuse: A Unified Multiple Complex Scenes Infrared and Visible Image Fusion Framework](https://mlsanity.com/paper/2402.02096/) (2024)
- [Instruction-Driven Fusion of Infrared-Visible Images: Tailoring for Diverse Downstream Tasks](https://mlsanity.com/paper/2411.09387/) (2024)
- [ControlFusion: A Controllable Image Fusion Framework with Language-Vision Degradation Prompts](https://mlsanity.com/paper/2503.23356/) (2025)
- [Deep Unfolding Multi-modal Image Fusion Network via Attribution Analysis](https://mlsanity.com/paper/2502.01467/) (2025)
- [DEAL: Data-Efficient Adversarial Learning for High-Quality Infrared Imaging](https://mlsanity.com/paper/2503.00905/) (2025)
- [Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks](https://mlsanity.com/paper/2402.15959/) (2024)
- [AWM-Fuse: Multi-Modality Image Fusion for Adverse Weather via Global and Local Text Perception](https://mlsanity.com/paper/2508.16881/) (2025)
- [MMA-UNet: A Multi-Modal Asymmetric UNet Architecture for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2404.17747/) (2024)
- [Enhancing Infrared Small Target Detection Robustness with Bi-Level Adversarial Framework](https://mlsanity.com/paper/2309.01099/) (2023)
- [A Semantic-Aware and Multi-Guided Network for Infrared-Visible Image Fusion](https://mlsanity.com/paper/2407.06159/) (2024)
- [textrmA^textrm2RNet: Adversarial Attack Resilient Network for Robust Infrared and Visible Image Fusion](https://mlsanity.com/paper/2412.09954/) (2024)
- [Customized Fusion: A Closed-Loop Dynamic Network for Adaptive Multi-Task-Aware Infrared-Visible Image Fusion](https://mlsanity.com/paper/2604.08924/) (2026)
- [CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception](https://mlsanity.com/paper/2309.01102/) (2023)
- [Rethinking the Evaluation of Visible and Infrared Image Fusion](https://mlsanity.com/paper/2410.06811/) (2024)
- [RLBayes: a Bayesian Network Structure Learning Algorithm via Reinforcement Learning-Based Search Strategy](https://mlsanity.com/paper/2504.05167/) (2025)
- [MultiTaskVIF: Segmentation-oriented visible and infrared image fusion via multi-task learning](https://mlsanity.com/paper/2505.06665/) (2025)
- [MAFS: Masked Autoencoder for Infrared-Visible Image Fusion and Semantic Segmentation](https://mlsanity.com/paper/2509.11817/) (2025)
- [MdaIF: Robust One-Stop Multi-Degradation-Aware Image Fusion with Language-Driven Semantics](https://mlsanity.com/paper/2511.12525/) (2025)
- [Adversarial Patch Generation for Visual-Infrared Dense Prediction Tasks via Joint Position-Color Optimization](https://mlsanity.com/paper/2603.00266/) (2026)
- [Diffuse to Detect: Bi-Level Sample Rebalancing with Pseudo-Label Diffusion for Point-Supervised Infrared Small-Target Detection](https://mlsanity.com/paper/2605.20766/) (2026)
- [Multi-modality Image Fusion under Adverse Weather: Mask-Guided Feature Restoration and Interaction](https://mlsanity.com/paper/2606.26812/) (2026)

## Abstract (arXiv)

Infrared and visible image fusion is a powerful technique that combines complementary information from different modalities for downstream semantic perception tasks. Existing learning-based methods show remarkable performance, but are suffering from the inherent vulnerability of adversarial attacks, causing a significant decrease in accuracy. In this work, a perception-aware fusion framework is proposed to promote segmentation robustness in adversarial scenes. We first conduct systematic analyses about the components of image fusion, investigating the correlation with segmentation robustness under adversarial perturbations. Based on these analyses, we propose a harmonized architecture search with a decomposition-based structure to balance standard accuracy and robustness. We also propose an adaptive learning strategy to improve the parameter robustness of image fusion, which can learn effective feature extraction under diverse adversarial perturbations. Thus, the goals of image fusion (textiti.e., extracting complementary features from source modalities and defending attack) can be realized from the perspectives of architectural and learning strategies. Extensive experimental results demonstrate that our scheme substantially enhances the robustness, with gains of 15.3% mIOU of segmentation in the adversarial scene, compared with advanced competitors. The source codes are available at https://github.com/LiuZhu-CV/PAIF.