Physics-oriented adversarial attacks on SAR image target recognition

Jiahao Cui, wang Guo, Run Shao, tiandong Shi, Haifeng Li

Keywords: Adversarial attack; SAR target recognition; physical model; deep neural network

TL;DR: The core idea of this method is to add adversarial scatterers in the physical domain to cover the salient areas where the classifier recognizes SAR targets, thereby reducing the recognition performance of the classifier.

Abstract:

SAR target recognition algorithms based on deep neural networks are widely used in key tasks such as wartime reconnaissance, environmental monitoring, but the security of SAR systems is also vulnerable to adversarial examples. The imaging process for SAR images in the physical world is dissimilar to that of optical images because SAR imaging is solely regulated by imaging equations rather than the what-you-see-is-what-you-get principle. As a result, generating SAR adversarial examples in the physical world requires considering the
changes in SAR imaging equations that happen after deploying physical devices. Thus, this study proposes a Physics-oriented adversarial attacks on SAR image target recognition. The proposed algorithm distinguishes itself through two key features: (1) SAR-BagNet is utilized to identify the salient regions of SAR targets recognized by classifiers, allowing for the exact position and size determination of the adversarial scatterers and enhancing interpretability; (2) Dynamic step size optimization, which is based on the difference equation, continuously refines the electromagnetic parameters, structural parameters, and texture parameters of the adversarial scatterers, leading to a higher search efficiency. In the simulation experiment, the generated adversarial examples can reduce the accuracy of the classifier to recognize the simulated image from 100 % to 14.4 %, thus verifying the method proposed in this paper.

Submission Number: 75