  The advent of Generative Adversarial Network (GAN) architectures has given
anyone the ability of generating incredibly realistic synthetic imagery. The
malicious diffusion of GAN-generated images may lead to serious social and
political consequences (e.g., fake news spreading, opinion formation, etc.). It
is therefore important to regulate the widespread distribution of synthetic
imagery by developing solutions able to detect them. In this paper, we study
the possibility of using Benford's law to discriminate GAN-generated images
from natural photographs. Benford's law describes the distribution of the most
significant digit for quantized Discrete Cosine Transform (DCT) coefficients.
Extending and generalizing this property, we show that it is possible to
extract a compact feature vector from an image. This feature vector can be fed
to an extremely simple classifier for GAN-generated image detection purpose.
