# Revisiting Adversarial Training under Hyperspectral Image

arXiv: https://arxiv.org/abs/2510.01014 · HTML: https://mlsanity.com/paper/2510.01014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 50 references that are in the map.
- First posted on arXiv 1 Oct 2025.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [Unlabeled Data Improves Adversarial Robustness](https://mlsanity.com/paper/1905.13736/) (2019)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples](https://mlsanity.com/paper/2010.03593/) (2020)
- [Data Augmentation Can Improve Robustness](https://mlsanity.com/paper/2111.05328/) (2021)
- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Probabilistic Margins for Instance Reweighting in Adversarial Training](https://mlsanity.com/paper/2106.07904/) (2021)
- [CFA: Class-wise Calibrated Fair Adversarial Training](https://mlsanity.com/paper/2303.14460/) (2023)
- [HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2404.09178/) (2024)
- [Randomized Adversarial Training via Taylor Expansion](https://mlsanity.com/paper/2303.10653/) (2023)
- [Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark](https://mlsanity.com/paper/2202.07054/) (2022)
- [AI Security for Geoscience and Remote Sensing: Challenges and Future Trends](https://mlsanity.com/paper/2212.09360/) (2022)
- [On the Over-Memorization During Natural, Robust and Catastrophic Overfitting](https://mlsanity.com/paper/2310.08847/) (2023)
- [Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image Captioning](https://mlsanity.com/paper/2312.01191/) (2023)
- [Adaptive Perturbation for Adversarial Attack](https://mlsanity.com/paper/2111.13841/) (2021)
- [TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions](https://mlsanity.com/paper/2503.01924/) (2025)
- [Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models](https://mlsanity.com/paper/2307.16865/) (2023)
- [Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders](https://mlsanity.com/paper/2207.07803/) (2022)
- [Dilated FCN: Listening Longer to Hear Better](https://mlsanity.com/paper/1907.11956/) (2019)

## Abstract (arXiv)

Recent studies have shown that deep learning-based hyperspectral image (HSI) classification models are highly vulnerable to adversarial attacks, posing significant security risks. Although most approaches attempt to enhance robustness by optimizing network architectures, these methods often rely on customized designs with limited scalability and struggle to defend against strong attacks. To address this issue, we introduce adversarial training (AT), one of the most effective defense strategies, into the hyperspectral domain. However, unlike conventional RGB image classification, directly applying AT to HSI classification introduces unique challenges due to the high-dimensional spectral signatures and strong inter-band correlations of hyperspectral data, where discriminative information relies on subtle spectral semantics and spectral-spatial consistency that are highly sensitive to adversarial perturbations. Through extensive empirical analyses, we observe that adversarial perturbations and the non-smooth nature of adversarial examples can distort or even eliminate important spectral semantic information. To mitigate this issue, we propose two hyperspectral-specific AT methods, termed AT-HARL and AT-RA. Specifically, AT-HARL exploits spectral characteristic differences and class distribution ratios to design a novel loss function that alleviates semantic distortion caused by adversarial perturbations. Meanwhile, AT-RA introduces spectral data augmentation to enhance spectral diversity while preserving spatial smoothness. Experiments on four benchmark HSI datasets demonstrate that the proposed methods achieve competitive performance compared with state-of-the-art approaches under adversarial attacks.