SATEN: SAR adversarial attack using targets to evaluate neural networks

Alexander Doom; Christopher Szul; Richard Borth

doi:10.1117/12.2665983

13 June 2023 SATEN: SAR adversarial attack using targets to evaluate neural networks

Alexander Doom, Christopher Szul, Richard Borth

Proceedings Volume 12520, Algorithms for Synthetic Aperture Radar Imagery XXX; 125200F (2023) https://doi.org/10.1117/12.2665983
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Synthetic Aperture Radar (SAR) imaging provides useful remote sensing capabilities because of its ability to image day-or-night and through clouds by using radar waves. However, understanding SAR vulnerabilities is important in developing data exploitation techniques that are resistant to “spoofing.” “Spoofing” is a type of attack where a virtual object is created in a SAR image by coherently adding the expected radar returns from a target into radar returns from the background. This research explored the effects of spoofing on Convolutional Neural Network (CNN) models for vehicle classification from the SAMPLE V2 data set. CNN models trained on SAR images with real targets in the scene were not able to accurately generalize to images with virtual targets in the scene; however, a model trained on real data identified spoofed images with an accuracy over 95.0% based on the confidence value outputs and a known proportion of spoofed images. Furthermore, a specialized training methodology enabled a CNN model to classify images as real or spoofed with an accuracy of more than 99.9% and classify the vehicle type with an accuracy of over 99.5%. This research determined the effects of real and spoofed SAR images on CNN models and what methods could be leverage to improve model performance.

Conference Presentation

Citation Download Citation

Alexander Doom, Christopher Szul, and Richard Borth "SATEN: SAR adversarial attack using targets to evaluate neural networks", Proc. SPIE 12520, Algorithms for Synthetic Aperture Radar Imagery XXX, 125200F (13 June 2023); https://doi.org/10.1117/12.2665983

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