A hyperspectral adversarial attack that combines block-wise spatial-spectral transformations with a variance-weighted feature divergence loss improves transferability over MI-FGSM baselines.
Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning ap- proach,
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Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence
A hyperspectral adversarial attack that combines block-wise spatial-spectral transformations with a variance-weighted feature divergence loss improves transferability over MI-FGSM baselines.