Classifying digital prints according to their production process using image analysis and artificial neural networks

Jack Tchan

doi:10.1117/12.382180

7 April 2000 Classifying digital prints according to their production process using image analysis and artificial neural networks

Jack Tchan

Author Affiliations +

Proceedings Volume 3973, Optical Security and Counterfeit Deterrence Techniques III; (2000) https://doi.org/10.1117/12.382180
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

A human expert observer can be employed to identify the production source of a print. The observer achieves this task by visual inspection of the print using a microscope. However, there are cases when the expert observer fails to identify correctly the production source. It is for this reason that the development of a method which can identify the production source is under consideration. This paper discusses the initial stages of the project which focuses on the development of a system that can classify prints from three different digital printing process. The system comprised an image analyzer that supplied image data from the print samples for initial analysis using a data pre- processing program and artificial neural networks which then used the pre-processed data to produce the classification models. The three different digital printing processes employed in this investigation were laser printing, optical photocopying and inkjet printing. Print samples were obtained from a range of laser printers,,optical photocopiers and inkjet printers. The prints used in the investigation were of a monochrome image of a square. The results show that the system is capable of classifying prints accurately for the range of printing machines and the image used in the trials.

Citation Download Citation

Jack Tchan "Classifying digital prints according to their production process using image analysis and artificial neural networks", Proc. SPIE 3973, Optical Security and Counterfeit Deterrence Techniques III, (7 April 2000); https://doi.org/10.1117/12.382180

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