  The possibility of carrying out a meaningful forensics analysis on printed
and scanned images plays a major role in many applications. First of all,
printed documents are often associated with criminal activities, such as
terrorist plans, child pornography pictures, and even fake packages.
Additionally, printing and scanning can be used to hide the traces of image
manipulation or the synthetic nature of images, since the artifacts commonly
found in manipulated and synthetic images are gone after the images are printed
and scanned. A problem hindering research in this area is the lack of large
scale reference datasets to be used for algorithm development and benchmarking.
Motivated by this issue, we present a new dataset composed of a large number of
synthetic and natural printed face images. To highlight the difficulties
associated with the analysis of the images of the dataset, we carried out an
extensive set of experiments comparing several printer attribution methods. We
also verified that state-of-the-art methods to distinguish natural and
synthetic face images fail when applied to print and scanned images. We
envision that the availability of the new dataset and the preliminary
experiments we carried out will motivate and facilitate further research in
this area.
