Deep neural networks have demonstrated excellent performance in SAR target detection tasks but remain susceptible to adversarial attacks. Existing SAR-specific attack methods can effectively deceive detectors; however, they often introduce noticeable perturbations and are largely confined to digital domain, neglecting physical implementation constrains for attacking SAR systems. In this paper, a novel Adversarial Attenuation Patch (AAP) method is proposed that employs energy-constrained optimization strategy coupled with an attenuation-based deployment framework to achieve a seamless balance between attack effectiveness and stealthiness. More importantly, AAP exhibits strong potential for physical realization by aligning with signal-level electronic jamming mechanisms. Experimental results show that AAP effectively degrades detection performance while preserving high imperceptibility, and shows favorable transferability across different models. This study provides a physical grounded perspective for adversarial attacks on SAR target detection systems and facilitates the design of more covert and practically deployable attack strategies. The source code is made available at https://github.com/boremycin/SAAP.

翻译：深度神经网络在SAR目标检测任务中展现了优异的性能，但仍易受对抗攻击影响。现有针对SAR系统的攻击方法虽能有效欺骗检测器，但往往引入明显扰动，且大多局限于数字域，忽略了攻击SAR系统所需的物理实现约束。本文提出了一种新颖的对抗衰减补丁（AAP）方法，该方法采用能量约束优化策略，并结合基于衰减的部署框架，实现了攻击有效性与隐蔽性之间的无缝平衡。更重要的是，AAP通过顺应信号级电子干扰机制，展现出强大的物理实现潜力。实验结果表明，AAP在保持高度不可感知性的同时，有效降低了检测性能，并表现出跨模型的良好迁移性。本研究为SAR目标检测系统的对抗攻击提供了物理层面的视角，并促进了更隐蔽且实际可部署攻击策略的设计。源代码已发布于https://github.com/boremycin/SAAP。