提出可物理实现的雷达对抗扰动贴片，隐蔽且有效干扰目标检测。
Towards Physically Realizable Adversarial Attenuation Patch against SAR Object Detection
- 基于能量约束与衰减机制设计对抗贴片，平衡攻击效果与隐蔽性。
- 实验显示贴片显著降低检测性能，且在不同模型间具有强迁移性。
- 贴片设计符合电子干扰原理，适合真实战场环境部署。
深度神经网络在合成孔径雷达（SAR）目标检测任务中表现优异，但易受对抗攻击影响。现有针对SAR的攻击方法虽能有效欺骗检测器，但常引入明显扰动，且局限于数字域，忽视了实际物理部署的约束。本文提出一种新型对抗衰减贴片（Adversarial Attenuation Patch, AAP），采用能量受限的优化策略与基于衰减的部署框架，在攻击效果与隐蔽性之间实现无缝平衡。更重要的是，AAP的设计与信号级电子干扰机制一致，具备良好的物理实现潜力。实验表明，该方法在显著降低检测性能的同时保持高不可察觉性，并展现出优异的跨模型迁移能力。本研究为SAR目标检测系统的对抗攻击提供了物理可行视角，推动更隐蔽、可实际部署的攻击策略设计。代码已开源：https://github.com/boremycin/SAAP。
原文摘要 · Abstract (English)
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.
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