Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating Natural Adversarial Hyperspectral examples with a modified
  Wasserstein GAN   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.

11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg