Adversarial Attenuation Patch Attack for SAR Object Detection

2026-04-01

AI Summary

This work addresses critical limitations in existing adversarial attacks on synthetic aperture radar (SAR) object detection, which often produce conspicuous, physically unrealizable perturbations and fail to align with SAR-specific electromagnetic interference mechanisms. To overcome these issues, the authors propose a novel adversarial attack method based on energy-constrained optimization and attenuative patch deployment, for the first time bridging adversarial perturbation generation with signal-level electronic interference principles inherent to SAR systems. By explicitly incorporating SAR imaging characteristics, the method generates highly imperceptible patches under strict energy constraints, achieving strong transferability across models and physical deployability. Experimental results demonstrate that the proposed approach significantly degrades detection performance across multiple state-of-the-art detectors while maintaining minimal perceptibility of the injected perturbations.

Abstract

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.

Problem

• SAR object detection
• adversarial attack
• physical implementation
• stealthiness
• imperceptibility

Innovation

• Adversarial Attenuation Patch
• SAR object detection
• physical adversarial attack
• energy-constrained optimization
• electronic jamming