SRAW-Attack: Space-Reweighted Adversarial Warping Attack for SAR Target Recognition

TL;DR

This paper introduces SRAW, a novel adversarial attack method for SAR target recognition that effectively degrades model performance while maintaining stealthiness through optimized spatial deformation.

Contribution

The paper presents SRAW, a new space-reweighted adversarial warping attack that improves stealthiness and transferability against SAR-ATR systems, addressing limitations of existing methods.

Findings

SRAW significantly reduces SAR-ATR accuracy.

SRAW outperforms existing adversarial attacks in stealthiness.

SRAW demonstrates high transferability across models.

Abstract

Synthetic aperture radar (SAR) imagery exhibits intrinsic information sparsity due to its unique electromagnetic scattering mechanism. Despite the widespread adoption of deep neural network (DNN)-based SAR automatic target recognition (SAR-ATR) systems, they remain vulnerable to adversarial examples and tend to over-rely on background regions, leading to degraded adversarial robustness. Existing adversarial attacks for SAR-ATR often require visually perceptible distortions to achieve effective performance, thereby necessitating an attack method that balances effectiveness and stealthiness. In this paper, a novel attack method termed Space-Reweighted Adversarial Warping (SRAW) is proposed, which generates adversarial examples through optimized spatial deformation with reweighted budgets across foreground and background regions. Extensive experiments demonstrate that SRAW significantly degrades the performance of state-of-the-art SAR-ATR models and consistently outperforms existing methods in terms of imperceptibility and adversarial transferability. Code is made available at https://github.com/boremycin/SAR-ATR-TransAttack.