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Multimedia Tools and Applications

, Volume 77, Issue 20, pp 27543–27587 | Cite as

Decision-theoretic model to identify printed sources

  • Min-Jen TsaiEmail author
  • Imam Yuadi
  • Yu-Han Tao
Article

Abstract

When trying to identify a printed forged document, examining digital evidence can prove to be a challenge. Over the past several years, digital forensics for printed document source identification has begun to be increasingly important which can be related to the investigation and prosecution of many types of crimes. Unlike invasive forensic approach which requires a fraction of the printed document as the specimen for verification, noninvasive forensic technique uses the optical mechanism to explore the relationship between the scanned images and the source printer. To explore the relationship between source printers and images obtained by the scanner, the proposed decision-theoretical approach utilizes image processing techniques and data exploration methods to calculate many important statistical features, including: Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, the Wiener filter, the Gabor filter, Haralick, and SFTA features. Consequently, the proposed aggregation method intensively applies the extracted features and decision-fusion model of feature selections for classification. In addition, the impact of different paper texture or paper color for printed sources identification is also investigated. In the meantime, the up-to-date techniques based on deep learning system is developed by Convolutional Neural Networks (CNNs) which can learn the features automatically to solve the complex image classification problem. Both systems have been compared and the experimental results indicate that the proposed system achieve the overall best accuracy prediction for image and text input and is superior to the existing approaches. In brief, the proposed decision-theoretical model can be very efficiently implemented for real world digital forensic applications.

Keywords

Decision fusion Scanner Feature filters Feature selection Support Vector Machines (SVM) Deep learning Convolutional Neural Networks (CNNs) 

Notes

Acknowledgments

This work was partially supported by the National Science Council in Taiwan, Republic of China, under NSC104-2410-H-009-020-MY2 and NSC106-2410-H-009-022-.

The authors would like to thank the anonymous reviewers with their valuable comments to improve the quality of this manuscript. Special thanks to Jin-Sheng Yin and Goang-Jiun Wang at National Chiao Tung University who help the revision and the software experiments.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Information ManagementNational Chiao Tung UniversityHsin-ChuTaiwan, Republic of China
  2. 2.Department of Information and Library ScienceAirlangga UniversitySurabayaIndonesia

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