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Towards Adversarial Training under Hyperspectral Images

Weihua Zhang, Chengze Jiang, Jie Gui, Lu Dong

TL;DR

The paper addresses the vulnerability of hyperspectral image classification to adversarial attacks and the limitations of architecture-based defenses. It transfers adversarial training to the hyperspectral domain, identifies a classification imbalance issue caused by spectral distortion, and introduces AT-RA by integrating RandAugment to diversify spectral information while preserving spatial smoothness. AT-RA significantly improves robustness (e.g., +21.34% against AutoAttack and +18.78% against PGD-50) and modestly boosts benign accuracy (+2.68%), with applicability to both standard and fast adversarial training; ablation shows the necessity of combining multiple augmentation types. The work provides a practical, scalable defense foundation for robust hyperspectral analysis and highlights the importance of spectral-domain data augmentation in mitigating adversarial effects.

Abstract

Recent studies have revealed that hyperspectral classification models based on deep learning are highly vulnerable to adversarial attacks, which pose significant security risks. Although several approaches have attempted to enhance adversarial robustness by modifying network architectures, these methods often rely on customized designs that limit scalability and fail to defend effectively against strong attacks. To address these challenges, we introduce adversarial training to the hyperspectral domain, which is widely regarded as one of the most effective defenses against adversarial attacks. Through extensive empirical analyses, we demonstrate that while adversarial training does enhance robustness across various models and datasets, hyperspectral data introduces unique challenges not seen in RGB images. Specifically, we find that adversarial noise and the non-smooth nature of adversarial examples can distort or eliminate important spectral semantic information. To mitigate this issue, we employ data augmentation techniques and propose a novel hyperspectral adversarial training method, termed AT-RA. By increasing the diversity of spectral information and ensuring spatial smoothness, AT-RA preserves and corrects spectral semantics in hyperspectral images. Experimental results show that AT-RA improves adversarial robustness by 21.34% against AutoAttack and 18.78% against PGD-50 while boosting benign accuracy by 2.68%.

Towards Adversarial Training under Hyperspectral Images

TL;DR

The paper addresses the vulnerability of hyperspectral image classification to adversarial attacks and the limitations of architecture-based defenses. It transfers adversarial training to the hyperspectral domain, identifies a classification imbalance issue caused by spectral distortion, and introduces AT-RA by integrating RandAugment to diversify spectral information while preserving spatial smoothness. AT-RA significantly improves robustness (e.g., +21.34% against AutoAttack and +18.78% against PGD-50) and modestly boosts benign accuracy (+2.68%), with applicability to both standard and fast adversarial training; ablation shows the necessity of combining multiple augmentation types. The work provides a practical, scalable defense foundation for robust hyperspectral analysis and highlights the importance of spectral-domain data augmentation in mitigating adversarial effects.

Abstract

Recent studies have revealed that hyperspectral classification models based on deep learning are highly vulnerable to adversarial attacks, which pose significant security risks. Although several approaches have attempted to enhance adversarial robustness by modifying network architectures, these methods often rely on customized designs that limit scalability and fail to defend effectively against strong attacks. To address these challenges, we introduce adversarial training to the hyperspectral domain, which is widely regarded as one of the most effective defenses against adversarial attacks. Through extensive empirical analyses, we demonstrate that while adversarial training does enhance robustness across various models and datasets, hyperspectral data introduces unique challenges not seen in RGB images. Specifically, we find that adversarial noise and the non-smooth nature of adversarial examples can distort or eliminate important spectral semantic information. To mitigate this issue, we employ data augmentation techniques and propose a novel hyperspectral adversarial training method, termed AT-RA. By increasing the diversity of spectral information and ensuring spatial smoothness, AT-RA preserves and corrects spectral semantics in hyperspectral images. Experimental results show that AT-RA improves adversarial robustness by 21.34% against AutoAttack and 18.78% against PGD-50 while boosting benign accuracy by 2.68%.

Paper Structure

This paper contains 24 sections, 4 equations, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Adversarial Defense in the Hyperspectral Domain
  4. Adversarial Training
  5. Adversarial Training for Hyperspectral Images
  6. Preliminaries
  7. Benign Example Pre-training Module
  8. Adversarial-Benign Loss Module
  9. Effects of Different Components
  10. Limitation of Adversarial Training
  11. Study of the Classification Imbalance Issue
  12. Spectral Analysis of Benign Examples
  13. Spectral Analysis of Adversarial Examples
  14. Addressing the classification Imbalance Issue
  15. Experiments
  16. ...and 9 more sections

Figures (2)

  • Figure 1: (a) Spectral curve of 'Meadows' class benign examples. (b) Spectral curve of 'Bare soil' class benign examples. (c) Spectral curve of an example of 'Meadows' adversarial examples. (d) Spectral curve of 'Meadows' class adversarial examples. (e) Spectral curve of 'Bare soil' class adversarial examples. (f) Spectral curve of an example of 'Bare soil' adversarial examples. 'Upper Bound' and 'Lower Bound' represent the maximum and minimum values of the intensity value on the spectrum respectively.
  • Figure 2: (a) Individual effects of single data augmentation method on benign accuracy. (b) Individual effects of single data augmentation method on adversarial robustness. (c) Influence of different combinations of data augmentation methods on benign accuracy. (d) Influence of different combinations of data augmentation methods on adversarial robustness.