  The recent computer graphics developments have upraised the quality of the
generated digital content, astonishing the most skeptical viewer. Games and
movies have taken advantage of this fact but, at the same time, these advances
have brought serious negative impacts like the ones yielded by fakeimages
produced with malicious intents. Digital artists can compose artificial images
capable of deceiving the great majority of people, turning this into a very
dangerous weapon in a timespan currently know as Fake News/Post-Truth" Era. In
this work, we propose a new approach for dealing with the problem of detecting
computer generated images, through the application of deep convolutional
networks and transfer learning techniques. We start from Residual Networks and
develop different models adapted to the binary problem of identifying if an
image was or not computer generated. Differently from the current
state-of-the-art approaches, we don't rely on hand-crafted features, but
provide to the model the raw pixel information, achieving the same 0.97 of
state-of-the-art methods with two main advantages: our methods show more stable
results (depicted by lower variance) and eliminate the laborious and manual
step of specialized features extraction and selection.
