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Recently, generative adversarial networks (GANs) have achieved stunningrealism, fooling even human observers. Indeed, the popular tongue-in-cheekwebsite {\small \url{ http://thispersondoesnotexist.com}}, taunts users withGAN generated images that seem too real to believe. On the other hand, GANs doleak information about their training data, as evidenced by membership attacksrecently demonstrated in the literature. In this work, we challenge theassumption that GAN faces really are novel creations, by constructing asuccessful membership attack of a new kind. Unlike previous works, our attackcan accurately discern samples sharing the same identity as training sampleswithout being the same samples. We demonstrate the interest of our attackacross several popular face datasets and GAN training procedures. Notably, weshow that even in the presence of significant dataset diversity, an overrepresented person can pose a privacy concern.

This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces