ADVERSARIAL ROBUSTNESS OF DEEP LEARNING METHODS FOR SAR IMAGE CLASSIFICATION: AN EXPLAINABILITY VIEW

The application of deep learning in the synthetic aperture radar (SAR) field is becoming increasingly widespread, but its black-box nature and the existence of adversarial samples limit its practical utility. In order to enhance the explainability and security of deep learning models, we initially selected 3 different deep convolutional neural network (DCNN) structures for training in SAR image classification. Subsequently, we applied Madry Defense Method to obtain robust models, and used 2 adversarial attack methods to attack DCNN classifiers. Finally, we employed 3 explainable artificial intelligence (XAI) methods to explain the predictions of different DCNN classifiers. Across different datasets and DCNNs, the Madry Defense Method helps classification models to reduce Infidelity and focus more on feature regions rich in semantic information when making decisions. The experimental results provide new insights into the adversarial robustness of DCNN classifiers in SAR image classification from an explainability viewpoint.