Graph contrastive learning based adversarial training for SAR image classification

Xu Wang; Tian Ye; Rajgopal Kannan; Viktor Prasanna

doi:10.1117/12.3014067

7 June 2024 Graph contrastive learning based adversarial training for SAR image classification

Xu Wang, Tian Ye, Rajgopal Kannan, Viktor Prasanna

Proceedings Volume 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI; 130320O (2024) https://doi.org/10.1117/12.3014067
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

In recent studies, Graph Neural Network (GNN) has been shown to be vulnerable to various adversarial attacks in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). Due to its important roles in military fields, the robustness of the GNN model raises severe security concerns. In this work, we propose a Graph Contrastive Learning based Adversarial Training for SAR Image Classification. By training the model with adversarial samples generated from Projected Gradient Descent Attack during the Contrastive Learning step, we demonstrate that our model can smooth the representation space and suppress the distortion caused by adversarial attacks, thus making better predictions. By formulating the problem as a multi-objective optimization task, our model achieves 98.1% accuracy on clean samples 85.2% accuracy on adversarial samples, both outperforming state-of-the-art models on the MSTAR dataset.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xu Wang, Tian Ye, Rajgopal Kannan, and Viktor Prasanna "Graph contrastive learning based adversarial training for SAR image classification", Proc. SPIE 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI, 130320O (7 June 2024); https://doi.org/10.1117/12.3014067

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