I have read the paper and there are plenty of useful references and points: related work, the 11 CNN based image generators models, and the discussion part. B

I have read the paper and there are plenty of useful references and points: related work, the 11 CNN based image generators models, and the discussion part. But sadly I could not obtain a clear picture of what is the difference between their detector and a baseline one. There are some minor points and references about upsampling, downsampling, resizing, cropping and fourier spectra comparison across generators, but those seems to be just comments and comparison and not crucial points in the construction of the detector. Furthermore data augmentation doesn't play a big role, they say that it usually improves (a little) the detector. As a math person I like to get some more meat from papers, but here it seems that little tricks allow then to win the game. Perhaps that is the way (little or no math involved) to make advances. Well, at least they say that shallow methods modify the fingerprint of the fourier spectra so that now you can't detect which is the generator of the image. Perhaps the "universal word" was what captured my attention.