论文标题
用改良的瓦斯坦甘纳
Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN
论文作者
论文摘要
对抗性示例是一个热门话题，因为它们有能力欺骗分类器的预测。有两种创建此类示例的策略，一个使用攻击的分类器的梯度，而另一个仅需要访问CLAS-Sifier的预测。当分类器未充分了解时，这尤其吸引人（黑匣子模型）。在本文中，我们提出了一种新方法，该方法能够从第二个范式之后的真实数据中生成自然的对抗示例。基于生成的对抗网络（GAN）[5]，它重新介绍了真实的数据经验分布，以鼓励分类器生成广告范围的示例。我们通过在遥感数据集上生成对抗性高光谱特征来提供方法的概念证明。
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.
