Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN

Authors: Jean-Christophe Burnel, Kilian Fatras, Nicolas Courty
Published: 2020-01-27

Source: https://arxiv.org/abs/2001.09993

PDF: https://arxiv.org/pdf/2001.09993

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敵対的サンプル 生成的敵対ネットワーク データ生成手法

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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.