FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification

arXiv:2404.03225v1 Announce Type: cross
Abstract: Deep Learning (DL) Models for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), while delivering improved performance, have been shown to be quite vulnerable to adversarial attacks. Existing works improve robustness by training models on adversarial samples. However, by focusing mostly on attacks that manipulate images randomly, they neglect the real-world feasibility of such attacks. In this paper, we propose FACTUAL, a novel Contrastive Learning framework for Adversarial Training and robust SAR classification. FACTUAL consists …

abstract adversarial adversarial attacks arxiv attacks classification cs.cv cs.lg deep learning framework however image novel performance radar recognition robust robustness samples synthetic synthetic aperture radar training training models type vulnerable