Back to Search Start Over

Boosting Adversarial Transferability with Shallow-Feature Attack on SAR Images

Authors :
Gengyou Lin
Zhisong Pan
Xingyu Zhou
Yexin Duan
Wei Bai
Dazhi Zhan
Leqian Zhu
Gaoqiang Zhao
Tao Li
Source :
Remote Sensing, Vol 15, Iss 10, p 2699 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Adversarial example generation on Synthetic Aperture Radar (SAR) images is an important research area that could have significant impacts on security and environmental monitoring. However, most current adversarial attack methods on SAR images are designed for white-box situations by end-to-end means, which are often difficult to achieve in real-world situations. This article proposes a novel black-box targeted attack method, called Shallow-Feature Attack (SFA). Specifically, SFA assumes that the shallow features of the model are more capable of reflecting spatial and semantic information such as target contours and textures in the image. The proposed SFA generates ghost data packages for input images and generates critical features by extracting gradients and feature maps at shallow layers of the model. The feature-level loss is then constructed using the critical features from both clean images and target images, which is combined with the end-to-end loss to form a hybrid loss function. By fitting the critical features of the input image at specific shallow layers of the neural network to the target critical features, our attack method generates more powerful and transferable adversarial examples. Experimental results show that the adversarial examples generated by the SFA attack method improved the success rate of single-model attack under a black-box scenario by an average of 3.73%, and 4.61% after combining them with ensemble-model attack without victim models.

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.6e9046b73d3942aca123ff3c54023bd7
Document Type :
article
Full Text :
https://doi.org/10.3390/rs15102699