Title: A forensic approach: identification of source printer through deep learning

Authors: Kanica Chugh; Pooja Ahuja

Addresses: School of Doctoral Studies and Research, National Forensic Sciences University, Gandhinagar, Gujarat, India ' School of Forensic Science, National Forensic Science University, Gandhinagar, 382007, India

Abstract: Forensic document forgery investigations have elevated the need for source identification for printed documents during the past few years. It is necessary to create a reliable and acceptable safety testing instrument to determine the credibility of printed materials. The proposed system in this study uses a neural network to detect the original printer used in forensic document forgery investigations. The study uses a deep neural network method, which relies on the quality, texture, and accuracy of images printed by various models of Canon and HP printers. The datasets were trained and tested to predict the accuracy using logical function, with the goal of creating a reliable and acceptable safety testing instrument for determining the credibility of printed materials. The technique classified the model with 95.1% accuracy. The proposed method for identifying the source of the printer is a non-destructive technique.

Keywords: forensic document analysis; printed documents; deep learning; convolutional neural network; CNN; printer identification.

DOI: 10.1504/IJESDF.2024.142030

International Journal of Electronic Security and Digital Forensics, 2024 Vol.16 No.6, pp.775 - 798

Received: 02 May 2023
Accepted: 03 Aug 2023
Published online: 07 Oct 2024 *

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