Adversarial attacks on deep-learning-based SAR image target recognition. (15th July 2020)

• Record Type: Journal Article
• Authors: Huang, Teng; Zhang, Qixiang; Liu, Jiabao; Hou, Ruitao; Wang, Xianmin; Li, Ya
• Is Part Of: Journal of network and computer applications. Volume 162(2020)
• Publication Date: 2020-07-15
• Subjects: SAR -- Deep learning -- Convolutional neural network -- Adversarial example; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2020.102632
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Synthetic aperture radar (SAR) image target recognition has consistently been a research hotspot in the field of radar image interpretation. Compared with traditional target recognition algorithms, SAR target recognition algorithms based on deep learning offer end-to-end feature learning, which can effectively improve the target recognition rate, making them an important method for radar target recognition. However, recent research shows that optical image recognition methods based on deep learning are vulnerable to adversarial examples. In SAR image target recognition, whether adversarial examples exist for deep learning algorithms is still an open question. This paper uses three mainstream algorithms to generate adversarial examples to attack three classical deep learning algorithms for SAR image target recognition. The experiments involve publicly real SAR images for white-box and black-box attacks. The results show that SAR target recognition algorithms based on deep learning are potentially vulnerable to adversarial examples.