Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN

Jean-Christophe Burnel 1 Kilian Fatras 1 Nicolas Courty 1
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
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.
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Submitted on : Saturday, January 25, 2020 - 12:02:38 PM
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  • HAL Id : hal-02447625, version 1
  • ARXIV : 2001.09993

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Jean-Christophe Burnel, Kilian Fatras, Nicolas Courty. Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN. C&ESAR, Nov 2019, Rennes, France. ⟨hal-02447625⟩

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