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Register a new userGenerating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN
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Jean-Christophe Burnel
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Authors
Jean-Christophe Burnel
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Adversarial examples are a hot topic due to their abilities to fool a classifiers prediction. There are two strategies to create such examples, one uses the attacked classifiers gradients, while the other only requires access to the clas-sifiers prediction. This is particularly appealing when the classifier is not full known (black box model). In this paper, we present a new method which is able to generate natural adversarial examples from the true data following the second paradigm. Based on Generative Adversarial Networks (GANs) [5], it reweights the true data empirical distribution to encourage the classifier to generate ad-versarial examples. We provide a proof of concept of our method by generating adversarial hyperspectral signatures on a remote sensing dataset.
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