Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense

TL;DR

This paper introduces a novel SAR-specific adversarial attack method guided by scattering models, which generates electromagnetic scattering perturbations to test and improve the robustness of SAR target recognition DNNs.

Contribution

It proposes the SMGAA algorithm utilizing a parametric scattering model and gradient optimization, enhancing adversarial attack effectiveness and robustness in SAR ATR systems.

Findings

Adversarial scatterers are more robust to perturbations and transformations.

The method effectively fools SAR classifiers and aids in robust model training.

Evaluations on MSTAR dataset demonstrate improved attack and defense capabilities.

Abstract

Deep Neural Networks (DNNs) based Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems have shown to be highly vulnerable to adversarial perturbations that are deliberately designed yet almost imperceptible but can bias DNN inference when added to targeted objects. This leads to serious safety concerns when applying DNNs to high-stake SAR ATR applications. Therefore, enhancing the adversarial robustness of DNNs is essential for implementing DNNs to modern real-world SAR ATR systems. Toward building more robust DNN-based SAR ATR models, this article explores the domain knowledge of SAR imaging process and proposes a novel Scattering Model Guided Adversarial Attack (SMGAA) algorithm which can generate adversarial perturbations in the form of electromagnetic scattering response (called adversarial scatterers). The proposed SMGAA consists of two parts: 1) a parametric scattering model and corresponding imaging method and 2) a customized gradient-based optimization algorithm. First, we introduce the effective Attributed Scattering Center Model (ASCM) and a general imaging method to describe the scattering behavior of typical geometric structures in the SAR imaging process. By further devising several strategies to take the domain knowledge of SAR target images into account and relax the greedy search procedure, the proposed method does not need to be prudentially finetuned, but can efficiently to find the effective ASCM parameters to fool the SAR classifiers and facilitate the robust model training. Comprehensive evaluations on the MSTAR dataset show that the adversarial scatterers generated by SMGAA are more robust to perturbations and transformations in the SAR processing chain than the currently studied attacks, and are effective to construct a defensive model against the malicious scatterers.