On the use of Benford’s law to detect GAN-generated images

Niccolò Bonettini, Paolo Bestagini, Simone Milani, Stefano Tubaro

Abstract:

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.