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

• URL: http://arxiv.org/abs/2308.03979v1
• Date: Tue, 8 Aug 2023 01:55:44 GMT
• Title: PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation
• Authors: Zhu Liu, Jinyuan Liu, Benzhuang Zhang, Long Ma, Xin Fan, Risheng Liu
• Abstract summary: We propose a perception-aware fusion framework to promote segmentation robustness in adversarial scenes. We show that our scheme substantially enhances the robustness, with gains of 15.3% mIOU, compared with advanced competitors.
• Score: 50.556961575275345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 (\textit{i.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.

Related papers

• From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion [66.33467192279514]
  We introduce a text-guided multi-modality image fusion method that leverages the high-level semantics from textual descriptions to integrate semantics from infrared and visible images. Our method not only produces visually superior fusion results but also achieves a higher detection mAP over existing methods, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2023-12-31T08:13:47Z)
• Searching a Compact Architecture for Robust Multi-Exposure Image Fusion [55.37210629454589]
  Two major stumbling blocks hinder the development, including pixel misalignment and inefficient inference. This study introduces an architecture search-based paradigm incorporating self-alignment and detail repletion modules for robust multi-exposure image fusion. The proposed method outperforms various competitive schemes, achieving a noteworthy 3.19% improvement in PSNR for general scenarios and an impressive 23.5% enhancement in misaligned scenarios.
  arXiv  Detail & Related papers  (2023-05-20T17:01:52Z)
• CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion [72.8898811120795]
  We propose a coupled contrastive learning network, dubbed CoCoNet, to realize infrared and visible image fusion. Our method achieves state-of-the-art (SOTA) performance under both subjective and objective evaluation.
  arXiv  Detail & Related papers  (2022-11-20T12:02:07Z)
• Interactive Feature Embedding for Infrared and Visible Image Fusion [94.77188069479155]
  General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention. We propose a novel interactive feature embedding in self-supervised learning framework for infrared and visible image fusion.
  arXiv  Detail & Related papers  (2022-11-09T13:34:42Z)
• Robustness and invariance properties of image classifiers [8.970032486260695]
  Deep neural networks have achieved impressive results in many image classification tasks. Deep networks are not robust to a large variety of semantic-preserving image modifications. The poor robustness of image classifiers to small data distribution shifts raises serious concerns regarding their trustworthiness.
  arXiv  Detail & Related papers  (2022-08-30T11:00:59Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• Contextual Fusion For Adversarial Robustness [0.0]
  Deep neural networks are usually designed to process one particular information stream and susceptible to various types of adversarial perturbations. We developed a fusion model using a combination of background and foreground features extracted in parallel from Places-CNN and Imagenet-CNN. For gradient based attacks, our results show that fusion allows for significant improvements in classification without decreasing performance on unperturbed data.
  arXiv  Detail & Related papers  (2020-11-18T20:13:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.