
Printer forensics is a specialized field within digital and document forensics that focuses on identifying the source printer of a printed document through intrinsic and extrinsic characteristics. As printers play a crucial role in both legitimate and malicious activities ranging from document authentication to the dissemination of forged or anonymous materials, the need for robust forensic techniques has become increasingly important. This paper provides a comprehensive overview of the current landscape in printer forensics, including the classification of methods used for source identification, such as mechanical defect analysis, texture pattern recognition, and embedded code detection. Both traditional image processing techniques and recent advancements leveraging machine learning and deep neural networks are examined. Additionally, we explore the challenges associated with dataset availability, print-scan noise, and cross-model generalization. By surveying existing methodologies and the public limitations of current approaches, we identify emerging trends and propose potential directions for future research in the field.

Counterfeiting of currency globally remains a significant problem to this day. According to the authorities, a large portion of this fake currency is produced by Small Office or Home Office inkjet printers. In this paper, we explain why a previously developed machine learning based Printer Identification System works with high accuracy and we investigate to improve the stability and generalization of the classifier. We study the print patterns of 8 inkjet printers from different manufacturers. We look at the features of the data by reducing its dimensions using Principle Component Analysis. This shows significant separation between printers which implies that the Deep Neural Network was able to pick up on key differences. The results are also comparable with that of reducing dimensions with Linear Discriminant Analysis. The model however does have some limitations regarding ink density and print media. It always classifies an image amongst the trained printers and does not show anomalies. For this, we consider the Gaussian distribution of target printers to see how the probabilities fared when trained and fitted separately for each printer, thus having a set of images that do not fit in with any of the printers for which the classifier was trained. The results acquired from these methods have contributed to making a more real-world implementation of our classifier, named LAPIS (machine-Learning Applied Printer Identification System), for printer forensics.