In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework. One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them. The other strategy learns formal specifications from the real data and shows that fake samples violate the specifications of the real data. We show that fake samples produced with GANs have a universal signature that can be used to identify fake samples. We provide results on MNIST, CIFAR10, music and speech data.
We show strategies to easily identify fake samples generated with the Generative Adversarial Network framework. Show that fake samples created with common generative adversarial network (GAN) implementations are easily identified using various statistical techniques.  The paper proposes statistics to identify fake data generated using GANs based on simple marginal statistics or formal specifications automatically generated from real data.