Japanese character based printed source identification

Min-Jen Tsai, Chien Lun Hsu, Jin Sheng Yin, Imam Yuadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Due to the advances of digital technology, digital contents are widely accepted all over the world. Since the criminal counterfeits and copyright infringement is still under study for the new digital media, there is an increasing need called digital forensics, which involves the collection and analysis of the original digital device, has become an important issue. This study offers a universal approach to extract the important statistical features from Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filter, Wiener filter and Gabor filter to identify the printer source for Japanese characters. The average experimental results achieves high identification rate at 94.23% which is significantly superior to the existing known method of using GLCM at 77.31%. The result is also better than the technique for Chinese character identification method at 88.77%. This superior testing performance demonstrates that the proposed identification technique is very effective for Japanese character based source laser printer identification.

Original languageEnglish
Title of host publication2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2800-2803
Number of pages4
ISBN (Electronic)9781479983919
DOIs
StatePublished - 27 Jul 2015
EventIEEE International Symposium on Circuits and Systems, ISCAS 2015 - Lisbon, Portugal
Duration: 24 May 201527 May 2015

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2015-July
ISSN (Print)0271-4310

Conference

ConferenceIEEE International Symposium on Circuits and Systems, ISCAS 2015
CountryPortugal
CityLisbon
Period24/05/1527/05/15

Keywords

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

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