Source Identification for Printed Documents

Min-Jen Tsai, Mam Yuadi, Yu Han Tao, Jin Sheng Yin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Technological advances in digitization with a variety of image manipulation techniques enable the creation of printed documents illegally. Correspondingly, many researchers conduct studies in determining whether the document printed counterfeit or original. This study examines the several statistical feature sets from Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, Wiener filter, Gabor filter, Haralick and fractal filters to identify text and image document by using support vector machine (SVM) and decision fusion of feature selection. The average experimental results achieves that the image document is higher identification rate than text document. In summary, the proposed method outperforms the previous researches and it is a promising technique that can be implemented in real forensics for printed documents.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 3rd International Conference on Collaboration and Internet Computing, CIC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages54-58
Number of pages5
ISBN (Electronic)9781538625651
DOIs
StatePublished - 9 Dec 2017
Event3rd IEEE International Conference on Collaboration and Internet Computing, CIC 2017 - San Jose, United States
Duration: 15 Oct 201717 Oct 2017

Publication series

NameProceedings - 2017 IEEE 3rd International Conference on Collaboration and Internet Computing, CIC 2017
Volume2017-January

Conference

Conference3rd IEEE International Conference on Collaboration and Internet Computing, CIC 2017
CountryUnited States
CitySan Jose
Period15/10/1717/10/17

Keywords

  • Discrete Wavelet Transform (DWT)
  • Forensics
  • Fractal Filter
  • Gabor Filter
  • GLCM
  • Haralick Filter
  • Spatial Filter
  • Support Vector Machines (SVM)
  • Wiener Filter

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