Classification of Inkjet Printers based on Droplet Statistics
This work addresses document forensics for security applications, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of identifying inkjet printer models from printed documents by analyzing droplet patterns, achieving classification of both manufacturers and specific models using neural networks on a new dataset.
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.