  As neural networks become able to generate realistic artificial images, they
have the potential to improve movies, music, video games and make the internet
an even more creative and inspiring place. Yet, the latest technology
potentially enables new digital ways to lie. In response, the need for a
diverse and reliable method toolbox arises to identify artificial images and
other content. Previous work primarily relies on pixel-space CNNs or the
Fourier transform. To the best of our knowledge, synthesized fake image
analysis and detection methods based on a multi-scale wavelet representation,
localized in both space and frequency, have been absent thus far. The wavelet
transform conserves spatial information to a degree, which allows us to present
a new analysis. Comparing the wavelet coefficients of real and fake images
allows interpretation. Significant differences are identified. Additionally,
this paper proposes to learn a model for the detection of synthetic images
based on the wavelet-packet representation of natural and GAN-generated images.
Our lightweight forensic classifiers exhibit competitive or improved
performance at comparatively small network sizes, as we demonstrate on the
FFHQ, CelebA and LSUN source identification problems. Furthermore, we study the
binary FaceForensics++ fake-detection problem.
