Exploring The Robustness Of Deep Image Despeckling Models in an Adversarial Perspective

The rapid development of deep learning has significantly advanced Synthetic Aperture Radar (SAR) despeckling techniques. However, as the uncertainty and vulnerability of the network structure is ignited by the fuse of adversarial attacks, its authenticity and widespread applicability are subsequently drawn into question. This study examines the robustness and performance of deep learning-based image despeckling models under a denoising-PGD adversarial attack. Furthermore, we investigate the impact of varying feature extraction approaches on model performance, with the goal of providing reliable guidelines for model training.