Adversarial Attenuation Patch Attack for SAR Object Detection

Computer Science > Computer Vision and Pattern Recognition [Submitted on 1 Apr 2026] Adversarial Attenuation Patch Attack for SAR Object Detection Yiming Zhang, Weibo Qin, Feng Wang 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 this https URL. Comments: 5 pages, 4 figures. Source code is available at this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR) Cite as: arXiv:2604.00887 [cs.CV]   (or arXiv:2604.00887v1 [cs.CV] for this version)   https://doi.org/10.48550/arXiv.2604.00887 Focus to learn more Submission history From: Yiming Zhang [view email] [v1] Wed, 1 Apr 2026 13:34:31 UTC (1,326 KB) Access Paper: view license Current browse context: cs.CV < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CR References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)