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The advent of deep learning has brought a significant improvement in thequality of generated media. However, with the increased level of photorealism,synthetic media are becoming hardly distinguishable from real ones, raisingserious concerns about the spread of fake or manipulated information over theInternet. In this context, it is important to develop automated tools toreliably and timely detect synthetic media. In this work, we analyze thestate-of-the-art methods for the detection of synthetic images, highlightingthe key ingredients of the most successful approaches, and comparing theirperformance over existing generative architectures. We will devote specialattention to realistic and challenging scenarios, like media uploaded on socialnetworks or generated by new and unseen architectures, analyzing the impact ofsuitable augmentation and training strategies on the detectors' generalizationability.

Are GAN generated images easy to detect? A critical analysis of the state-of-the-art