Synthetic aperture radar (SAR) imagery exhibits intrinsic information sparsity due to its unique electromagnetic scattering mechanism. Despite the widespread adoption of deep neural network (DNN)-based SAR automatic target recognition (SAR-ATR) systems, they remain vulnerable to adversarial examples and tend to over-rely on background regions, leading to degraded adversarial robustness. Existing adversarial attacks for SAR-ATR often require visually perceptible distortions to achieve effective performance, thereby necessitating an attack method that balances effectiveness and stealthiness. In this paper, a novel attack method termed Space-Reweighted Adversarial Warping (SRAW) is proposed, which generates adversarial examples through optimized spatial deformation with reweighted budgets across foreground and background regions. Extensive experiments demonstrate that SRAW significantly degrades the performance of state-of-the-art SAR-ATR models and consistently outperforms existing methods in terms of imperceptibility and adversarial transferability. Code is made available at https://github.com/boremycin/SAR-ATR-TransAttack.

翻译：合成孔径雷达（SAR）图像因其独特的电磁散射机制而表现出固有的信息稀疏性。尽管基于深度神经网络（DNN）的SAR自动目标识别（SAR-ATR）系统已被广泛采用，但它们仍然容易受到对抗样本的攻击，并且倾向于过度依赖背景区域，导致对抗鲁棒性下降。现有的SAR-ATR对抗攻击通常需要视觉上可感知的畸变才能达到有效性能，因此需要一种平衡有效性和隐蔽性的攻击方法。本文提出了一种新颖的攻击方法，称为空间重加权对抗性形变（SRAW），该方法通过优化的空间形变生成对抗样本，并在前景和背景区域之间重新分配形变预算。大量实验表明，SRAW能显著降低最先进的SAR-ATR模型的性能，并且在不可感知性和对抗可迁移性方面持续优于现有方法。代码发布于 https://github.com/boremycin/SAR-ATR-TransAttack。