  Advances in Artificial Intelligence and Image Processing are changing the way
people interacts with digital images and video. Widespread mobile apps like
FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to
produce extreme transformations on human face photos such gender swap, aging,
etc. The results are utterly realistic and extremely easy to be exploited even
for non-experienced users. This kind of media object took the name of Deepfake
and raised a new challenge in the multimedia forensics field: the Deepfake
detection challenge. Indeed, discriminating a Deepfake from a real image could
be a difficult task even for human eyes but recent works are trying to apply
the same technology used for generating images for discriminating them with
preliminary good results but with many limitations: employed Convolutional
Neural Networks are not so robust, demonstrate to be specific to the context
and tend to extract semantics from images. In this paper, a new approach aimed
to extract a Deepfake fingerprint from images is proposed. The method is based
on the Expectation-Maximization algorithm trained to detect and extract a
fingerprint that represents the Convolutional Traces (CT) left by GANs during
image generation. The CT demonstrates to have high discriminative power
achieving better results than state-of-the-art in the Deepfake detection task
also proving to be robust to different attacks. Achieving an overall
classification accuracy of over 98%, considering Deepfakes from 10 different
GAN architectures not only involved in images of faces, the CT demonstrates to
be reliable and without any dependence on image semantic. Finally, tests
carried out on Deepfakes generated by FACEAPP achieving 93% of accuracy in the
fake detection task, demonstrated the effectiveness of the proposed technique
on a real-case scenario.
