  Despite recent advances in Generative Adversarial Networks (GANs), with
special focus to the Deepfake phenomenon there is no a clear understanding
neither in terms of explainability nor of recognition of the involved models.
In particular, the recognition of a specific GAN model that generated the
deepfake image compared to many other possible models created by the same
generative architecture (e.g. StyleGAN) is a task not yet completely addressed
in the state-of-the-art. In this work, a robust processing pipeline to evaluate
the possibility to point-out analytic fingerprints for Deepfake model
recognition is presented. After exploiting the latent space of 50 slightly
different models through an in-depth analysis on the generated images, a proper
encoder was trained to discriminate among these models obtaining a
classification accuracy of over 96%. Once demonstrated the possibility to
discriminate extremely similar images, a dedicated metric exploiting the
insights discovered in the latent space was introduced. By achieving a final
accuracy of more than 94% for the Model Recognition task on images generated by
models not employed in the training phase, this study takes an important step
in countering the Deepfake phenomenon introducing a sort of signature in some
sense similar to those employed in the multimedia forensics field (e.g. for
camera source identification task, image ballistics task, etc).
