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

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.

Key Contributions

SRAW: a spatial warping-based adversarial attack that reweights perturbation budgets across foreground and background regions to exploit SAR imagery's information sparsity
Improved imperceptibility by concentrating deformation in background regions while maintaining adversarial effectiveness
Demonstrated superior adversarial transferability over existing SAR-ATR attack methods across state-of-the-art models

🛡️ Threat Analysis

Input Manipulation Attack

Proposes SRAW, a new adversarial example generation method using optimized spatial deformation with foreground/background budget reweighting to cause misclassification in DNN-based SAR-ATR models at inference time. Evaluates both attack effectiveness and adversarial transferability (black-box setting).