# Improving Hyperspectral Adversarial Robustness Under Multiple Attacks Source: [Improving Hyperspectral Adversarial Robustness Under Multiple Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:16:36.929Z Authors: Nicholas Soucy, Salimeh Yasaei Sekeh Published: 2022-10-28T18:21:45.000Z ## Abstract Semantic segmentation models classifying hyperspectral images (HSI) are vulnerable to adversarial examples. Traditional approaches to adversarial robustness focus on training or retraining a single network on attacked data, however, in the presence of multiple attacks these approaches decrease in performance compared to networks trained individually on each attack. To combat this issue we propose an Adversarial Discriminator Ensemble Network (ADE-Net) which focuses on attack type detection and adversarial robustness under a unified model to preserve per data-type weight optimally while robustifiying the overall network. In the proposed method, a discriminator network is used to separate data by attack type into their specific attack-expert ensemble network. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()