Adversarial Deception Against SAR Target Recognition Network
Fan Zhang, Tianying Meng, Deliang Xiang, Fei Ma, Xiaokun Sun, Yongsheng Zhou
Abstract
Synthetic Aperture Radar (SAR) automatic target recognition (ATR) technology is one of the key technologies to achieve intelligent interpretation for SAR images. With the rapid development of deep learning, deep neural networks have been successively used in SAR ATR and show priority in comparison with the conventional methods. Recently, more and more attention is paid to the robustness of deep learning based SAR ATR methods. The reason is that maliciously modified and imperceptible adversarial images can deceive the SAR ATR methods which are based on the deep neural networks. In this paper, we propose a novel SAR ATR adversarial deception algorithm, which fully considers the characteristics of SAR data. Our method can obtain the satisfactory perturbations with a higher deception success rate, a higher recognition confidence, and a smaller perturbation coverage than other state-of-the- art methods for the SAR images. Experimental results using the MSTAR dataset and OpenSARShip dataset demonstrate the effectiveness of our method. The proposed adversarial deception method can be used in the applications such as SAR dataset protection, SAR sensor design and SAR image quality evaluation.