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Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN
Abstract : Adversarial examples are a hot topic due to their abilities to fool a classifier's prediction. There are two strategies to create such examples, one uses the attacked classifier's gradients, while the other only requires access to the clas-sifier's 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.
Cited literature [10 references]
https://hal.archives-ouvertes.fr/hal-02447625
Contributor : Jean-Christophe Burnel Connect in order to contact the contributor
Submitted on : Saturday, January 25, 2020 - 12:02:38 PM
Last modification on : Wednesday, November 3, 2021 - 6:05:58 AM
Contributor : Jean-Christophe Burnel Connect in order to contact the contributor
Submitted on : Saturday, January 25, 2020 - 12:02:38 PM
Last modification on : Wednesday, November 3, 2021 - 6:05:58 AM
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