  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.
