文章基本信息

标题：Source Printer Identification with Microscopic Printing using Deep Learning
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作者：Anh-Thu Phan-Ho ; Quoc-Thông Nguyen ; Jérémy Patrix 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：10
页码：1177-1182
DOI：10.1016/j.ifacol.2022.09.549
语种：English
出版社：Elsevier
摘要：Due to the advent of digital technologies, it has become much easier to falsify printed documents for malicious purposes. Therefore, it is essential to research and develop efficient algorithms to distinguish authentic printed documents from fakes. A source printer identification technique is one of the methods to trace the source of the documents, thereby protecting the reliability and integrity of printed documents. This paper proposes a method to identify the source printers using Deep Learning approach. Particularly, we utilise a Convolutional Neural Network for features extraction from printed microscopic patterns of different printing sources. These extracted features are fed into a multi-class Support Vector Machine and Random Forest classification for the source identification. The actual printed dot patterns from common printing technologies such as conventional offset, waterless offset, electrophotographic are used to evaluate the effectiveness of our proposed approach. This study highlights the fact that printed patterns in gray images and features based on Convolutional Neural Network extraction help to reserve much more distinguishable characteristics of printing sources. The hybrid classification algorithms outperform the previous studies using hand-extracted features, with more than 99 % of samples correctly classified in the test set.
关键词：Convolutional Neural Network;Source Printer Identification;Microscopic Printing;Printed Document Authentication;Support Vector Machine;Random Forest