  The Deepfake phenomenon has become very popular nowadays thanks to the
possibility to create incredibly realistic images using deep learning tools,
based mainly on ad-hoc Generative Adversarial Networks (GAN). In this work we
focus on the analysis of Deepfakes of human faces with the objective of
creating a new detection method able to detect a forensics trace hidden in
images: a sort of fingerprint left in the image generation process. The
proposed technique, by means of an Expectation Maximization (EM) algorithm,
extracts a set of local features specifically addressed to model the underlying
convolutional generative process. Ad-hoc validation has been employed through
experimental tests with naive classifiers on five different architectures
(GDWCT, STARGAN, ATTGAN, STYLEGAN, STYLEGAN2) against the CELEBA dataset as
ground-truth for non-fakes. Results demonstrated the effectiveness of the
technique in distinguishing the different architectures and the corresponding
generation process.
