@inproceedings{23af3815fd7447bda5929bd4d44a515c,
title = "Exploring the Robustness of Deep Image Despeckling Models in an Adversarial Perspective",
abstract = "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.",
keywords = "SAR despeckling, adversarial attack, denoisng- PGD, image denoising network, speckle noise removal",
author = "Jie Ning and Yao Li and Zhichang Guo and Jiebao Sun and Shengzhu Shi and Enzhe Zhao and Boying Wu",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 ; Conference date: 07-07-2024 Through 12-07-2024",
year = "2024",
doi = "10.1109/IGARSS53475.2024.10641277",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8463--8467",
booktitle = "IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "美国",
}