  Detecting fake images is becoming a major goal of computer vision. This need
is becoming more and more pressing with the continuous improvement of synthesis
methods based on Generative Adversarial Networks (GAN), and even more with the
appearance of powerful methods based on Diffusion Models (DM). Towards this
end, it is important to gain insight into which image features better
discriminate fake images from real ones. In this paper we report on our
systematic study of a large number of image generators of different families,
aimed at discovering the most forensically relevant characteristics of real and
generated images. Our experiments provide a number of interesting observations
and shed light on some intriguing properties of synthetic images: (1) not only
the GAN models but also the DM and VQ-GAN (Vector Quantized Generative
Adversarial Networks) models give rise to visible artifacts in the Fourier
domain and exhibit anomalous regular patterns in the autocorrelation; (2) when
the dataset used to train the model lacks sufficient variety, its biases can be
transferred to the generated images; (3) synthetic and real images exhibit
significant differences in the mid-high frequency signal content, observable in
their radial and angular spectral power distributions.
