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

General GAN-generated image detection by data augmentation in fingerprint domain