A Novel Multi-size Block Benford’s Law Scheme for Printer Identification

  • Weina Jiang
  • Anthony T. S. Ho
  • Helen Treharne
  • Yun Q. Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)


Identifying the originating device for a given media, i.e. the type, brand, model and other characteristics of the device, is currently one of the important fields of digital forensics. This paper proposes a forensic technique based on the Benford’s law to identify the printer’s brand and model from the printed-and-scanned images at which the first digit probability distribution of multi-size block DCT coefficients are extracted that constitutes a feature vector as the input to support vector machine (SVM) classifier. The proposed technique is different from the traditional use of noise feature patterns appeared in the literature. It uses as few as nine numbers of forensic features representing each printer by leveraging properties of the Benford’s law for printer identification. Experiments conducted over electrophotographic (EP) printers and deskjet printers achieve an average of 96.0% classification rate of identification for five distinct printer brands and an average of 94.0% classification rate for six diverse printer models out of those five brands.


Digital forensics printer identification multi-size block based DCT coefficients Benford’s law composite signature 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Khanna, N., Mikkilineni, A.K., Chiu, G.T., Allebach, J.P., Delp, E.J.: Survey of scanner and printer forensics at purdue university. In: Srihari, S.N., Franke, K. (eds.) IWCF 2008. LNCS, vol. 5158, pp. 22–34. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Zhao, X., Ho, A.T.S., Shi, Y.Q.: Image forensics using generalized benfords law for accurate detection of unknown jpeg compression in watermarked images. In: 16th International Conference on Digital Signal Processing (DSP), Greece (July 2009)Google Scholar
  3. 3.
    Chiang, P.-J., Khanna, N., Mikkilineni, A., Segovia, M., Suh, S., Allebach, J., Chiu, G., Delp, E.: Printer and scanner forensics. IEEE Signal Processing Magazine 26, 72–83 (2009)CrossRefGoogle Scholar
  4. 4.
    Mikkilineni, A.K., Arslan, O., Chiang, P.-J., Kumontoy, R.M., Allebach, J.P., Chiu, G.T.-C., Delp, E.J.: Printer forensics using svm techniques. In: Proceedings of the IS&T’s NIP21: International Conference on Digital Printing Technologies, Baltimore, MD, vol. 21, pp. 223–226 (October 2005)Google Scholar
  5. 5.
    Mikkilineni, A.K., Chiang, P.-J., Ali, G.N., Chiu, G.T.-C., Allebach, J.P., Delp, E.J.: Printer identification based on graylevel co-occurrence features for security and forensic applications. In: Security, Steganography, and Watermarking of Multimedia Contents, pp. 430–440 (2005)Google Scholar
  6. 6.
    Nitin, K., Mikkilineni, A.K., Chiang, P.-J., Ortiz, M.V., Shah, V., Suh, S., Chiu, G.T.-C., Allebach, J.P., Delp, E.J.: Printer and sensor forensics. In: IEEE Workshop on Signal Processing Applications for Public Security and Forensics, Washington, D.C, USA, April 11-13 (2007)Google Scholar
  7. 7.
    Bulan, O., Mao, J., Sharma, G.: Geometric distortion signatures for printer identification. In: Proc. IEEE Intl. Conf. Acoustics Speech and Sig. Proc., Taipei, Taiwan, pp. 1401–1404 (2009)Google Scholar
  8. 8.
    Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Transactions on Information Forensics and Security 1, 205–214 (2006)CrossRefGoogle Scholar
  9. 9.
    Chen, M., Fridrich, J., Goljan, M., Lukas, J.: Determining image origin and integrity using sensor noise. IEEE Transactions on Information Forensics and Security 3, 74–90 (2008)CrossRefGoogle Scholar
  10. 10.
    Filler, T., Fridrich, J., Goljan, M.: Using sensor pattern noise for camera model identification. In: 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 1296–1299 (12-15, 2008)Google Scholar
  11. 11.
    Perez-Gonzalez, F., Heileman, G., Abdallah, C.: Benford’s law in image processing. In: Proc. IEEE International Conference on Image Processing, vol. 1, pp. 405–408 (2007)Google Scholar
  12. 12.
    Fu, D., Shi, Y.Q., Su, W.: A generalized Benford’s law for JPEG coefficients and its applications in image forensics. In: Proceedings of SPIE, vol. 6505, p. 65051L (2007)Google Scholar
  13. 13.
    Floyd, R., Steinberg, L.: An adaptive algorithm for spatial greyscale. Proceedings of the. Society for Information Display 17(2), 75–77 (1976)Google Scholar
  14. 14.
    Ulichney, R.: Digital Halftoning. MIT Press, Cambridge (1987)Google Scholar
  15. 15.
    Li, B., Shi, Y.Q., Huang, J.: Detecting double compressed jpeg image by using mode based first digit features. In: IEEE International Workshop on Multimedia Signal Processing (MMSP 2008), Queensland, Australia, pp. 730–735 (October 2008)Google Scholar
  16. 16.
    Chen, P.-H., Lin, C.-J.: LIBSVM: a library for support vector machines (2001) Software available at,

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Weina Jiang
    • 1
  • Anthony T. S. Ho
    • 1
  • Helen Treharne
    • 1
  • Yun Q. Shi
    • 2
  1. 1.Dept. of ComputingUniversity of Surrey GuildfordUK
  2. 2.Dept. of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewwarkUSA

Personalised recommendations