https://128.84.21.199/pdf/1807.04919.pdf

In this paper we show strategies to fake 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 and shows that fake samples violate the specifications of the real . 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 .



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