Universal Adversarial Patch Attacks on SAR Object Detection

Deep learning-based object detection has become a crucial component in the interpretation of Synthetic Aperture Radar (SAR) imagery, widely applied in monitoring and surveillance tasks. However, the robustness of these data-driven models against adversarial perturbations in complex electromagnetic environments remains an open question. This paper investigates the vulnerability of SAR object detectors to Universal Adversarial Patch (UAP) attacks. We design a compact, location-agnostic patch and optimize it using a detection-aware objective specifically formulated to suppress objectness confidence and bounding box regression scores. This allows the patch to degrade performance effectively without requiring precise target alignment or scene-specific adaptation. Experiments on the SAR-AIRcraft-1.0 dataset indicate a noticeable decline in detection metrics, particularly in Recall rates and mAP, with observed black-box transferability across different architectures. Supported by ablation studies on patch scale and position, these findings reveal the potential susceptibility of SAR detectors to localized interference, serving as a reference for future security assessments.