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Printer source identification by feature modeling in the total variable printer space.

Roozbeh Hamzehyan¹, Farbod Razzazi¹, Alireza Behrad²

¹Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Journal of Forensic Sciences

|August 25, 2021

Summary

This study introduces a novel machine learning approach for printer source identification using Local Binary Pattern (LBP) features. The method achieves high accuracy, advancing digital forensics capabilities.

Keywords:

i-vector printer forensics printer source identification probabilistic linear discriminant analysis support vector machine total variability printer space

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Area of Science:

Digital Forensics
Machine Learning
Image Analysis

Background:

Digital forensics is rapidly evolving with advancements in digital technology.
Printer source identification is a critical area within digital forensics.
Existing methods may be limited by language or computational cost.

Purpose of the Study:

To develop a novel, efficient, and language-independent method for printer source identification.
To leverage Local Binary Pattern (LBP) features for enhanced forensic analysis.
To reduce computational complexity in forensic document examination.

Main Methods:

Modeling primary Local Binary Pattern (LBP) features in printer space.
Extracting secondary features using joint factor analysis.
Employing low-dimensional i-vector features per document image, avoiding OCR.

Main Results:

The proposed algorithm achieved an accuracy of 98.48% in printer source identification.
The method effectively extracts discriminant information from sparse print texture.
The approach demonstrates comparable performance to state-of-the-art methods.

Conclusions:

The developed method offers a robust and efficient solution for printer source identification.
The language and character set independence broadens its applicability in digital forensics.
This technique significantly reduces computational cost and complexity.