Image Splicing Verification Based on Pixel-Based Alignment Method

  • Rimba Whidiana Ciptasari
  • Kyung-Hyune Rhee
  • Kouichi Sakurai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7809)


Due to the easy manipulation and alteration of digital images using widely available software tools, forgery detection is emerged as a primary goal in image forensics. A common form of manipulation is to combine parts of the image fragment into another different image to remove objects from the image. Inspired by the image registration concept, we exploit the correlation-based alignment method to automatically identify the spliced region in any fragment of the reference images. We show the efficacy of the proposed scheme on revealing the source of spliced regions. We anticipate this scheme to be the first concrete technique towards appropriate tools which are necessary for exposing digital forgeries.


Image splicing image alignment edge detection membership function interpolation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ng, T.T., Chang, S.F., Sun, Q.: Blind Detection of Photomontage using Higher Order Statistics. In: IEEE International Symposium on Circuits and Systems, vol. 5, pp. 688–691 (2004)Google Scholar
  2. 2.
    Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: ACM Multimedia and Security Workshop (2005)Google Scholar
  3. 3.
    Hsu, Y.-F., Chang, S.-F.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: IEEE International Conference on Multimedia and Expo. (ICME) (2006)Google Scholar
  4. 4.
    Chen, W., Shi, Y.Q., Su, W.: Image Splicing Detection using 2-D Phase Congruency and Statistical Moments of Characteristic Function. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 6505, art. No. 65050R. SPIE, Washington (2007)Google Scholar
  5. 5.
    Ye, S., Sun, Q., Chang, E.C.: Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact. In: IEEE International Conference on Multimedia and Expo. (ICME) (2007)Google Scholar
  6. 6.
    Dong, J., Wang, W., Tan, T., Shi, Y.Q.: Run-Length and Edge Statistics Based Approach for Image Splicing Detection. In: Kim, H.-J., Katzenbeisser, S., Ho, A.T.S. (eds.) IWDW 2008. LNCS, vol. 5450, pp. 76–87. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Ciptasari, R.W., Rhee, K.-H., Sakurai, K.: An Image Splicing Detection Based on Interpolation Analysis. In: Lin, W., Xu, D., Ho, A., Wu, J., He, Y., Cai, J., Kankanhalli, M., Sun, M.-T. (eds.) PCM 2012. LNCS, vol. 7674, pp. 390–401. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Farid, H.: Detecting Digital Forgeries Using Bispectral Analysis. Technical Report AIM-1657, AI Lab, Massachusetts Institute of Technology (1999)Google Scholar
  9. 9.
    Ng, T.T., Chang, S.F., Lin, C.Y., Sun, Q.: Passive-blind Image Forensics. In: Zeng, W., Yu, H., Lin, C.Y. (eds.) Multimedia Security Technologies for Digital Rights, ch. 15, pp. 383–412. cademic Press, Missouri (2006)CrossRefGoogle Scholar
  10. 10.
    Popescu, A.C., Farid, H.: Exposing Digital Forgeries by Detecting Traces of Re-sampling. IEEE Transaction on Signal Processing 53(2), 758–767 (2005)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Prasad, S., Ramakrishnan, K.R.: On Resampling Detection And Its Application To Detect Image Tampering. In: IEEE International Conference on Multimedia and Expo. (ICME) (2006)Google Scholar
  12. 12.
    Pan, X., Lyu, S.: Region Duplication Detection using Image Feature Matching. IEEE Transaction on Information Forensics and Security 5(4), 857–867 (2010)CrossRefGoogle Scholar
  13. 13.
    Farid, H., Lyu, S.: Higher-order Wavelet Statistics and their Application to Digital Forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision (in Conjunction with CVPR) (2003)Google Scholar
  14. 14.
    Avcibas, I., Bayram, S., Memon, N., Sankur, B., Ramkumar, M.: A Classifier Design for Detecting Image Manipulations. In: IEEE International Conference on Image Processing, ICIP (2004)Google Scholar
  15. 15.
    Bayram, S., Avcibas, I., Sankur, B., Memon, N.: Image Manipulation Detection. Journal of Electronic Imaging 15(4), 041102 (2006)Google Scholar
  16. 16.
    Sutthiwan, P., Shi, Y.Q., Zhao, H., Ng, T.-T., Su, W.: Markovian Rake Transform for Digital Image Tampering Detection. In: Shi, Y.Q., Emmanuel, S., Kankanhalli, M.S., Chang, S.-F., Radhakrishnan, R., Ma, F., Zhao, L. (eds.) Transaction on DHMS VI. LNCS, vol. 6730, pp. 1–17. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Wang, W., Farid, H.: Exposing digital forgeries in video by detecting duplication. In: Proceeding ACM Workshop on MMSec, Dallas, TX (2007)Google Scholar
  18. 18.
    Ng, T.T.: Statistical and Geometric Methods for Passive-blind Image Forensics. Ph.D. Dissertation, Columbia University (2007)Google Scholar
  19. 19.
    Szeliski, R.: Image Alignment and Stitching: A Tutorial. Computer Graphics and Vision 2(1), 1–104 (2006), doi:10.1561/0600000009MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Prentice Hall (2008)Google Scholar
  21. 21.
    Ye, S.M., Sun, Q.B., Chang, E.C.: Error resilient content-based image authentication over wireless channel. In: IEEE Int. Symp. Circuits and Systems (ISCAS), Kobe, Japan, pp. 2707–2710 (2005)Google Scholar
  22. 22.
    Ng, T.T., Chang, S., Sun, Q.: A data set of authentic and spliced image blocks. In: ADVENT Technical Report 203-2004-3. Columbia University (June 2004),

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rimba Whidiana Ciptasari
    • 1
    • 2
  • Kyung-Hyune Rhee
    • 3
  • Kouichi Sakurai
    • 1
  1. 1.Graduate School of Information Science and Electrical Engineering, Department of InformaticsKyushu UniversityFukuokaJapan
  2. 2.Faculty of InformaticsTelkom Institute of TechnologyBandungIndonesia
  3. 3.Department of IT Convergence and Application EngineeringPukyong National UniversityBusanKorea

Personalised recommendations