Sparse adversarial attack of SAR image

Image interpretation technology based on deep learning has achieved great success in many fields, and has gradually begun to be widely used in synthetic aperture radar image classification, detection, and segmentation problems. Existing SAR image classification deep learning models are prone to overfitting due to the small sample size of the training dataset, and small changes in samples can easily lead to model classification errors, which calls adversarial attack phenomena. In response to the above problems, this article explains the problems and challenges of SAR image adversarial attacks from three aspects: attack method, attack result and attack target. This article focuses on the sparsity of SAR images, specifically expounds the background of sparsity attacks and the manifestations of sparsity in SAR images, analyzes and summarizes common sparsity attack methods. The article verifies the effectiveness of the existing sparse attack methods on the MSTAR dataset, analyzes the calculation efficiency, success rate, time-consuming and other indicators of the algorithm, and prospects the SAR image classification sparse attack method.