CFP last date
21 October 2024
Reseach Article

A Survey of Image Processing Techniques for Identification of Printing Technology in Document Forensic Perspective

Published on None 2010 by M. Uma Devi, C. Raghavendra Rao, Arun Agarwal
Recent Trends in Image Processing and Pattern Recognition
Foundation of Computer Science USA
RTIPPR - Number 1
None 2010
Authors: M. Uma Devi, C. Raghavendra Rao, Arun Agarwal
887a502b-111b-4732-8f70-c808260ce643

M. Uma Devi, C. Raghavendra Rao, Arun Agarwal . A Survey of Image Processing Techniques for Identification of Printing Technology in Document Forensic Perspective. Recent Trends in Image Processing and Pattern Recognition. RTIPPR, 1 (None 2010), 9-15.

@article{
author = { M. Uma Devi, C. Raghavendra Rao, Arun Agarwal },
title = { A Survey of Image Processing Techniques for Identification of Printing Technology in Document Forensic Perspective },
journal = { Recent Trends in Image Processing and Pattern Recognition },
issue_date = { None 2010 },
volume = { RTIPPR },
number = { 1 },
month = { None },
year = { 2010 },
issn = 0975-8887,
pages = { 9-15 },
numpages = 7,
url = { /specialissues/rtippr/number1/970-93/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Recent Trends in Image Processing and Pattern Recognition
%A M. Uma Devi
%A C. Raghavendra Rao
%A Arun Agarwal
%T A Survey of Image Processing Techniques for Identification of Printing Technology in Document Forensic Perspective
%J Recent Trends in Image Processing and Pattern Recognition
%@ 0975-8887
%V RTIPPR
%N 1
%P 9-15
%D 2010
%I International Journal of Computer Applications
Abstract

This paper discusses about various image processing techniques and tools which are available for identification of printing technologies. Printing technology identification and associated problems in document forensics have been projected as challenges in image processing application. Various image processing approaches based on textures, spatial variation, HSV color space, spatial correlation, and feature based on histogram and some of the pattern recognition methods, like gray level co-occurrence matrix, roughness of the text, perimeter of edge are highlighted. This paper devotes more on one of the recent contribution, namely, Gaussian Variogram Model (GVM) for printer classification.

References
  1. Ordway Hilton. Scientific Examination of Questioned Documents. CRC Press, 1993.
  2. http://www.hp.com.
  3. http://www.fosterfreeman.com
  4. Nitin Khanna, Aravind K, Mikkilineni, Anthony F.Martone, Gazi N. Ali, George T.C. Chiu, Jan Allebach,and Edward J. Delp. A survey of forensic characterization methods for physical devices. In Digital Investigation3s, pages s17–s28, 2006
  5. Mikkilineni A. K., Pei-Ju. Chiang, Ali G. N., Chiu G.T., Allebach J. P., and Delp E. J. ’Printer Identification based on Graylevel Co-ocuurence Features for Security and Forensic Applications’. In Proceedings of the SPIE International Conference on Security, Volume 5681,, pages 430–440, Mar 2005.
  6. Ali G. N., Chiang P. J., Mikkilineni A. K., Chiu G.T.- C, Delp E.J., and Allebach J. P. ’Application of Principal Components Analysis and Gaussian Mixture Models to Printer Identification’. In Proceedings of the IS & T’s NIP20: International Conference on Digital Printing Technologies, pages Volume 20, pp.301–305, Nov 2004.
  7. http://www.eff.org/issues/printers.
  8. Harith D and Chakravarthy B. ’Identification of Printing Process using HSV Colour Space’. In Asian Conference on Computer Vision, pp 629-701,2006.
  9. Chakravarthy B and Haritha D. ’Classification of Liquid and Viscous Inks using HSV Color Space’. In Proceedings of Eight International Conference on Document Analysis and Recognition, 2005. pp 660-664.
  10. Gaurav Gupta, Sanjoy Kumar Saha, Shayok Chakraborty, Chandan Mazumdar, Document Frauds: Identification and Linking Fake Document to Scanners and Printers, Proceeding of the International conference on Computing Theory and Applications,ICCTA’07, IEEE, pp 497-501, 2007.
  11. Christoph H. Lampert, Lin Mei, and Thomas M. Breuel. ’Printing Technique Classification for Document Counterfeit Detection’. In IEEE International Conference on Computational Intelligence and Security, pages 639–644, Nov 2006.
  12. Christian Schulze, Marco Schreyer, Armin Stahl, and Thomas Breuel. ’Evaluation of Graylevel-Features for Printing Technique Classification in High- Throughput Document Management Systems’. In International Work shop on Computational Forensics, pages 35–46, Aug 2008.
  13. Gary K.Starkweather. Electronic color printing technology. In IEEE Proceedings of COMPCON’96-41st IEEE International Computer Conference, pages 435–439, 1996.
  14. M Uma Devi, Arun Agarwal and C.Raghavendra Rao ’Gaussian Variogram Model for Printing Technology Identification’ International Conference on Asian Modeling Symposium , pp 320-325,2009.
  15. http://www.goldensoftware.com
  16. A. Wijaya, P.R.Marpu, and R.Gloaguen. Geostatistical Texture Classification of Trophical Rainforest in Indonesia. In 5th ISPRS International Symposium on Spatial Data Quality, 2007.
  17. C.A.Coburn and A. C. B. Roberts. A multiscale texture analysis procedure for improved forest stand classification. International Journal of Remote Sensing, Vol.25:4287–4308, 2004.
  18. A. Jkomulska and K.C. Clarke. Variogram derived measures of textural image classification-application to large scale vegetation mapping. In Proceedings of the Third European Conference on Geostatistics for Environmental Applications, pages 345–355, 2000.
  19. E. Gringarten and C. V. Deutsch Teachers Aide: Variogram Interpretation and Modeling, Mathematical Geology, Vol.33 (4) pages 507-534, 2001.
  20. Y. Ramadevi, C. R. Rao, and Vivekchan Reddy. Decision tree induction using roughset theory comparative study. In Journal of Theoretical and Applied Information Technology, pages 110–114, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Image processing Document forensics-printing technique classification spatial statics