APNet: A Novel Antiperturbation Network for Robust Hyperspectral Image Classification Against Adversarial Attacks

Deep learning (DL) methods have achieved impressive performance in hyperspectral image (HSI) classification but are susceptible to adversarial attacks, which can lead to significant accuracy degradation. Although there has been encouraging progress in improving model robustness for HSI classificatio...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-14
Main Authors: Zhao, Lin, Zhang, Youlin, Shi, Chengzhong, Zhao, Minhui, Wu, Jianhui, Li, Wen
Format: Article
Language:English
Subjects:
Adversarial attack
adversarial defense
Classification algorithms
Deep learning
deep learning (DL)
Feature extraction
hyperspectral image (HSI) classification
Hyperspectral imaging
Image classification
Perturbation methods
Robustness
Spatial coherence
superpixel prior
Training
Transformers
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Summary:Deep learning (DL) methods have achieved impressive performance in hyperspectral image (HSI) classification but are susceptible to adversarial attacks, which can lead to significant accuracy degradation. Although there has been encouraging progress in improving model robustness for HSI classification, the existing approaches primarily concentrate on capturing global pixel-level dependencies while overlooking the intrinsic superpixel priors of HSI-namely, spatial smoothness and spectral correlations within spatially coherent regions. In addition, these methods often lack effective noise suppression mechanisms. To address these challenges, we introduce a novel antiperturbation network, APNet, designed for robust HSI classification against adversarial attacks. APNet incorporates a noise suppression module that includes a U-shaped unit (UsU) and a feature cascade unit (FCU) to extract clear pixel-level features at multiple scales. These features are then combined with superpixel priors, which serve as robust tokens for a Transformer encoder to capture global structural features. By fusing denoised pixel-level features with coarse-grained superpixel-level features, APNet significantly enhances the robustness of feature representations and the network's intrinsic resistance to adversarial attacks. Extensive experiments on three HSI benchmark datasets show that APNet outperforms the existing state-of-the-art techniques against across various attacks and perturbation intensities. In particular, APNet maintains stable classification performance even under high attack intensities.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3467088