Deepfaking it: experiments in generative, adversarial multispectral remote sensing

Christopher X. Ren; Amanda Ziemann; James Theiler; Juston Moore

doi:10.1117/12.2587753

23 April 2021 Deepfaking it: experiments in generative, adversarial multispectral remote sensing

Christopher X. Ren, Amanda Ziemann, James Theiler, Juston Moore

Proceedings Volume 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII; 117270M (2021) https://doi.org/10.1117/12.2587753
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

In this work we utilize generative adversarial networks (GANs) to synthesize realistic transformations for remote sensing imagery in the multispectral domain. Despite the apparent perceptual realism of the transformed images at a first glance, we show that a deep learning classifier can very easily be trained to differentiate between real and GAN-generated images, likely due to subtle but pervasive artifacts introduced by the GAN during the synthesis process. We also show that a very low-amplitude adversarial attack can easily fool the aforementioned deep learning classifier, although these types of attacks can be partially mitigated via adversarial training. Finally, we explore the features utilized by the classifier to differentiate real images from GAN-generated ones, and how adversarial training causes the classifier to focus on different, lower-frequency features.

Conference Presentation

Citation Download Citation

Christopher X. Ren, Amanda Ziemann, James Theiler, and Juston Moore "Deepfaking it: experiments in generative, adversarial multispectral remote sensing", Proc. SPIE 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, 117270M (23 April 2021); https://doi.org/10.1117/12.2587753

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