Improving Hyperspectral Adversarial Robustness using Ensemble Networks in the Presences of Multiple Attacks. (arXiv:2210.16346v1 [cs.LG])

Semantic segmentation of hyperspectral images (HSI) has seen great strides in
recent years by incorporating knowledge from deep learning RGB classification
models. Similar to their classification counterparts, semantic segmentation
models are vulnerable to adversarial examples and need adversarial training to
counteract them. 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 the performance compared
to networks trained individually on each attack. To combat …

adversarial attacks networks robustness