FACTUAL：一种基于对比学习的鲁棒SAR图像分类新框架
FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification
AI总结：
针对SAR ATR模型易受现实物理对抗攻击的问题，本文提出FACTUAL对比学习框架，融合OTSA等扰动进行有监督对抗预训练并级联线性分类器，在干净与扰动样本上均取得优于现有方法的准确率。
摘要：
用于合成孔径雷达（SAR）自动目标识别（ATR）的深度学习（DL）模型虽然带来了性能提升，但已被证明极易受到对抗攻击。现有工作通过在对抗样本上训练模型来增强鲁棒性。然而，这些工作主要关注随机操纵图像的攻击，忽视了此类攻击在现实世界中的可行性。本文提出FACTUAL，这是一种用于对抗训练和鲁棒SAR分类的新型对比学习框架。FACTUAL由两个部分组成：（1）不同于现有工作，本文提出一种新的扰动方案，纳入现实物理对抗攻击（如OTSA）以构建有监督对抗预训练网络。该网络利用类别标签，将干净图像和扰动图像共同聚类到信息更丰富的特征空间中。（2）在编码器后级联一个线性分类器，使用计算得到的表征预测目标标签。通过在干净样本和对抗样本上对模型进行预训练和微调，我们证明模型在两种情况下均取得较高预测准确率。模型在干净样本上达到99.7%的准确率，在扰动样本上达到89.6%的准确率，二者均优于以往最先进方法。
英文摘要：
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.
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