CNN-generated images are surprisingly easy to spot... for nowIn this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used. To test this, we collect a dataset consisting of fake images generated by 11 different CNN-based image generator models, chosen to span the space of commonly used architectures today (ProGAN, StyleGAN, BigGAN, CycleGAN,
http://arxiv.org/pdf/1511.04164v2
Fully Convolutional Networks for Semantic Segmentation
http://arxiv.org/pdf/1510.03820v3
