  Advancements in deep generative models such as generative adversarial
networks and variational autoencoders have resulted in the ability to generate
realistic images that are visually indistinguishable from real images, which
raises concerns about their potential malicious usage. In this paper, we
present an analysis of the high-frequency Fourier modes of real and deep
network generated images and show that deep network generated images share an
observable, systematic shortcoming in replicating the attributes of these
high-frequency modes. Using this, we propose a detection method based on the
frequency spectrum of the images which is able to achieve an accuracy of up to
99.2% in classifying real and deep network generated images from various GAN
and VAE architectures on a dataset of 5000 images with as few as 8 training
examples. Furthermore, we show the impact of image transformations such as
compression, cropping, and resolution reduction on the classification accuracy
and suggest a method for modifying the high-frequency attributes of deep
network generated images to mimic real images.

Image and Video Processing;Machine Learning;Machine Learning