Multiple Classifier Systems for Image Forgery Detection

  • Davide Cozzolino
  • Francesco Gargiulo
  • Carlo Sansone
  • Luisa Verdoliva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)


A large number of techniques have been proposed recently for forgery detection, based on widely different principles and processing tools. As a result, each technique performs well with some types of forgery, and under given hypotheses, and much worse in other situations. To improve robustness, one can merge the output of different techniques but it is not obvious how to balance the different sources of information. In this paper we consider and test several combining rules, working both at the abstract level and at measurement level, and providing information on both presence and location of suspect tampered regions. Experimental results on a suitable dataset of forged images show that a careful fusion of detector’s output largely outperforms individual detectors, and that measurement-level fusion methods are more effective than abstract-level ones.


Forgery detection digital forensics image tampering 


  1. 1.
    Barni, M., Costanzo, A.: A fuzzy approach to deal with uncertainty in image forensics. Signal Processing: Image Communication 27(9), 998–1010 (2012)Google Scholar
  2. 2.
    Bianchi, T., De Rosa, A., Piva, A.: Improved dct coefficient analysis for forgery localization in jpeg images. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2444–2447 (May 2011)Google Scholar
  3. 3.
    Chierchia, G., Parrilli, S., Poggi, G., Sansone, C., Verdoliva, L.: On the influence of denoising in PRNU based forgery detection. In: ACM Workshop on Multimedia in Forensics, Security and Intelligence, Firenze, Italy, pp. 117–122 (2010)Google Scholar
  4. 4.
    Cozzolino, D., Poggi, G., Sansone, C., Verdoliva, L.: A comparative analysis of forgery detection algorithms. In: Gimel’farb, G., Hancock, E., Imiya, A., Kuijper, A., Kudo, M., Omachi, S., Windeatt, T., Yamada, K. (eds.) SSPR&SPR 2012. LNCS, vol. 7626, pp. 693–700. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Farid, H.: Exposing Digital Forgeries From JPEG Ghosts. IEEE Transactions on Information Forensics and Security 4(1), 154–160 (2009)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Fontani, M., Bianchi, T., De Rosa, A., Piva, A., Barni, M.: A dempster-shafer framework for decision fusion in image forensics. In: IEEE International Workshop on Information Forensics and Security (WIFS), November 29-December 2, pp. 1–6 (2011)Google Scholar
  7. 7.
    Gallagher, A.: Detection of linear and cubic interpolation in jpeg compressed images. In: The 2nd Canadian Conference on Computer and Robot Vision, pp. 65–72 (May 2005)Google Scholar
  8. 8.
    Gordon, J., Shortliffe, E.: The dempster-shafer theory of evidence. In: Buchanan, B.G., Shortliffe, E. (eds.) Rule-Based Expert Systems, pp. 272–292 (1984)Google Scholar
  9. 9.
    Hsu, Y.F., Chang, S.F.: Statistical fusion of multiple cues for image tampering detection. In: Asilomar Conference on Signals, Systems, and Computers (2008)Google Scholar
  10. 10.
    Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)CrossRefGoogle Scholar
  11. 11.
    Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2004)Google Scholar
  12. 12.
    Li, W., Yuan, Y., Yu, N.: Passive detection of doctored JPEG image via block artifact grid extraction. Signal Processing 89(9), 1821–1829 (2009)CrossRefzbMATHGoogle Scholar
  13. 13.
    Lukas, J., Fridrich, J., Goljan, M.: Detecting digital image forgeries using sensor pattern noise. In: Proceedings of the SPIE, vol. 6072, SPIE (2006)Google Scholar
  14. 14.
    Mahdian, B., Saic, S.: Blind Authentication Using Periodic Properties of Interpolation. IEEE Transactions on Information Forensics and Security 3(3), 529–538 (2008)CrossRefGoogle Scholar
  15. 15.
    Piva, A.: An Overview on Image Forensics. ISRN Signal Processing pp. 1–22 (2012)Google Scholar
  16. 16.
    Schaefer, J., Stich, M.: Ucid - an uncompressed colour image database. In: Proc. SPIE, Storage and Retrieval Methods and Applications for Multimedia, pp. 472–480 (2004)Google Scholar
  17. 17.
    Xu, L., Krzyzak, A., Suen, C.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics 22(3), 418–435 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Davide Cozzolino
    • 1
  • Francesco Gargiulo
    • 1
  • Carlo Sansone
    • 1
  • Luisa Verdoliva
    • 1
  1. 1.DIETIUniversity of Naples Federico IIItaly

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