Fingerprints of Generative Models in the Frequency Domain
Tianyun Yang Juan Cao Danding Wang Chang Xu
It is verified in existing works that CNN-based generative models leave
unique fingerprints on generated images. There is a lack of analysis about how
they are formed in generative models. Interpreting network components in the
frequency domain, we derive sources for frequency distribution and grid-like
pattern discrepancies exhibited on the spectrum. These insights are leveraged
to develop low-cost synthetic models, which generate images emulating the
frequency patterns observed in real generative models. The resulting
fingerprint extractor pre-trained on synthetic data shows superior
transferability in verifying, identifying, and analyzing the relationship of
real CNN-based generative models such as GAN, VAE, Flow, and diffusion.