In this work, we investigate improving the generalizability of GAN-generated
image detectors by performing data augmentation in the fingerprint domain.
Specifically, we first separate the fingerprints and contents of the
GAN-generated images using an autoencoder based GAN fingerprint extractor,
followed by random perturbations of the fingerprints. Then the original
fingerprints are substituted with the perturbed fingerprints and added to the
original contents, to produce images that are visually invariant but with
distinct fingerprints. The perturbed images can successfully imitate images
generated by different GANs to improve the generalization of the detectors,
which is demonstrated by the spectra visualization. To our knowledge, we are
the first to conduct data augmentation in the fingerprint domain. Our work
explores a novel prospect that is distinct from previous works on spatial and
frequency domain augmentation. Extensive cross-GAN experiments demonstrate the
effectiveness of our method compared to the state-of-the-art methods in
detecting fake images generated by unknown GANs