  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 of two components: (1) Differing from existing works, a novel
perturbation scheme that incorporates realistic physical adversarial attacks
(such as OTSA) to build a supervised adversarial pre-training network. This
network utilizes class labels for clustering clean and perturbed images
together into a more informative feature space. (2) A linear classifier
cascaded after the encoder to use the computed representations to predict the
target labels. By pre-training and fine-tuning our model on both clean and
adversarial samples, we show that our model achieves high prediction accuracy
on both cases. Our model achieves 99.7% accuracy on clean samples, and 89.6% on
perturbed samples, both outperforming previous state-of-the-art methods.
