  Knowing the printer model used to print a given document may provide a
crucial lead towards identifying counterfeits or conversely verifying the
validity of a real document. Inkjet printers produce probabilistic droplet
patterns that appear to be distinct for each printer model and as such we
investigate the utilization of droplet characteristics including frequency
domain features extracted from printed document scans for the classification of
the underlying printer model. We collect and publish a dataset of high
resolution document scans and show that our extracted features are informative
enough to enable a neural network to distinguish not only the printer
manufacturer, but also individual printer models.
