  The advent of deep learning has brought a significant improvement in the
quality of generated media. However, with the increased level of photorealism,
synthetic media are becoming hardly distinguishable from real ones, raising
serious concerns about the spread of fake or manipulated information over the
Internet. In this context, it is important to develop automated tools to
reliably and timely detect synthetic media. In this work, we analyze the
state-of-the-art methods for the detection of synthetic images, highlighting
the key ingredients of the most successful approaches, and comparing their
performance over existing generative architectures. We will devote special
attention to realistic and challenging scenarios, like media uploaded on social
networks or generated by new and unseen architectures, analyzing the impact of
suitable augmentation and training strategies on the detectors' generalization
ability.
