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

2026-01-15

AI Summary

This work proposes a spatially reweighted adversarial deformation attack to address the vulnerability of synthetic aperture radar automatic target recognition (SAR-ATR) systems to adversarial perturbations and their limited robustness due to reliance on background regions. By integrating a spatial reweighting mechanism with adversarial deformations, the method allocates differentiated perturbation budgets to foreground and background areas, thereby optimizing the spatial deformation field. This approach achieves high attack success rates while significantly enhancing the imperceptibility of adversarial examples and their transferability across models. Experimental results demonstrate that the proposed method substantially reduces recognition accuracy across multiple state-of-the-art SAR-ATR models, outperforming existing techniques in both attack effectiveness and visual stealth.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• adversarial attack
• SAR target recognition
• imperceptibility
• adversarial robustness
• deep neural networks

Innovation

• adversarial attack
• spatial warping
• SAR target recognition
• imperceptibility
• transferability