AdverSAR: adversarial imagery examples generation for synthetic aperture radar dataset augmentation

Ali Ahmadibeni; Branndon Jones; Amir Shirkhodaie

doi:10.1117/12.2664713

13 June 2023 AdverSAR: adversarial imagery examples generation for synthetic aperture radar dataset augmentation

Ali Ahmadibeni, Branndon Jones, Amir Shirkhodaie

Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 125290M (2023) https://doi.org/10.1117/12.2664713
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

In this research work, by using the comprehensive IRIS simulated SAR dataset that includes ground, aerial, and marine vehicles, we explored and exploited different GAN-based techniques to increase the efficiency and effectiveness of the DL-based SAR image classifiers pre-trained based on synthetically generated SAR imagery datasets. Particularly, in this paper, we present three adversarial attach techniques on the DL classifiers. Then, we propose a streamlined generative model for properly training of SAR classifiers with less susceptibility to newly introduced adversarial examples. Lastly, we discuss the merits of our proposed methodologies and offer our future research directions for the further improvement of the proposed SAR-GAN-CNN model and summarize our future research contributions.

Conference Presentation

Citation Download Citation

Ali Ahmadibeni, Branndon Jones, and Amir Shirkhodaie "AdverSAR: adversarial imagery examples generation for synthetic aperture radar dataset augmentation", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 125290M (13 June 2023); https://doi.org/10.1117/12.2664713

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