Revisiting Adversarial Training under Hyperspectral Image

ArXi:2510.01014v2 Announce Type: replace 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