BAN: A Boundary-Aware Network Based on KAN for Robust Hyperspectral Image Classification Against Adversarial Attacks

Deep neural networks (DNNs) have achieved significant advancements in hyperspectral image (HSI) classification, enabling critical applications in environmental monitoring, medical imaging, and geological analysis. However, their vulnerability to adversarial attacks, particularly in the boundary regions between classes, remains a critical challenge. Existing defense methods often struggle to address these issues due to insufficient adaptability to irregular boundary patterns and larger perturbations in boundary regions. To bridge this gap, we propose a boundary-aware network (BAN), a novel adversarial defense network framework that integrates a multiscale deformable convolution (MSDC) module with a Kolmogorov–Arnold network (KAN). The MSDC module dynamically adjusts receptive fields across scales to capture discriminative spatial-spectral features, while the KAN architecture leverages its cubic spline-based smooth nonlinearity to suppress gradient-driven adversarial perturbations. By leveraging these components, BAN not only mitigates boundary-specific vulnerabilities but also enhances global robustness against diverse adversarial threats. Experimental results on three benchmark HSI datasets demonstrate that BAN outperforms state-of-the-art methods, maintaining high accuracy and robustness under various adversarial attack scenarios.