Run-Length and Edge Statistics Based Approach for Image Splicing Detection

  • Jing Dong
  • Wei Wang
  • Tieniu Tan
  • Yun Q. Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5450)


In this paper, a simple but efficient approach for blind image splicing detection is proposed. Image splicing is a common and fundamental operation used for image forgery. The detection of image splicing is a preliminary but desirable study for image forensics. Passive detection approaches of image splicing are usually regarded as pattern recognition problems based on features which are sensitive to splicing. In the proposed approach, we analyze the discontinuity of image pixel correlation and coherency caused by splicing in terms of image run-length representation and sharp image characteristics. The statistical features extracted from image run-length representation and image edge statistics are used for splicing detection. The support vector machine (SVM) is used as the classifier. Our experimental results demonstrate that the two proposed features outperform existing ones both in detection accuracy and computational complexity.


image splicing run-length edge detection characteristic functions support vector machine (SVM) 


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  1. 1.
    Gloe, T., Kirchner, M., Winkler, A., Behme, R.: Can we trust digital image foren- sics? In: Proceedings of the 15th international conference on Multimedia, pp. 78–86 (2007)Google Scholar
  2. 2.
    Rey, C., Dugelay, J.L.: A survey of watermarking algorithms for image authentication. EURASIP J. Appl. Signal Process. 2002(1), 613–621 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Yeung, N.M.: Digital watermarking introduction. CACM 41(7), 31–33 (1998)CrossRefGoogle Scholar
  4. 4.
    Fridrich, J.: Methods for tamper detection in digital images. In: Proceedings of the ACM Workshop on Multimedia and Security, pp. 19–23 (1999)Google Scholar
  5. 5.
    Fridrich, J., Soukal, D., Lukas, J.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic research Workshop (August 2003)Google Scholar
  6. 6.
    Luo, W., Qu, Z., Pan, F., Huang, J.: A survey of passive technology for digital image forensics. In: Front. Comput. Sciences of China, vol. 1(2)Google Scholar
  7. 7.
    Ng, T.T., Chang, S.F.: A model for image splicing. In: 2004 International Conference on Image Processing (ICIP 2004), pp. 1169–1172 (2004)Google Scholar
  8. 8.
    Ng, T.T., Chang, S.F., Sun, Q.: Blind detection of photomontage using higher order statistics. In: IEEE International Symposium on Circuits and Systems (2004)Google Scholar
  9. 9.
    Ng, T.T., Chang, S.F.: Blind detection of photomontage using higher order statistics. In: ADVENT Technical Report 201-2004-1. Columbia University (June 8, 2004)Google Scholar
  10. 10.
    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),
  11. 11.
    Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: ACM Multimedia and Security Workshop (2005)Google Scholar
  12. 12.
    Hsu, Y., Chang, S.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: IEEE ICME (July 2006)Google Scholar
  13. 13.
    Fu, D., Shi, Y.Q., Su, W.: Detection of image splicing based on hilbert-huang transform and moments of characteristic functions with wavelet decomposition. In: Shi, Y.Q., Jeon, B. (eds.) IWDW 2006. LNCS, vol. 4283, pp. 177–187. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Chen, W., Shi, Y.Q.: Image splicing detection using 2-d phase congruency and statistical moments of characteristic function. In: Imaging: Security, Steganography, and Watermarking of Multimedia Contents (January 2007)Google Scholar
  15. 15.
    Shi, Y.Q., Chen, C., Xuan, G.: Steganalysis versus splicing detection. In: Shi, Y.Q., Kim, H.-J., Katzenbeisser, S. (eds.) IWDW 2007. LNCS, vol. 5041, pp. 158–172. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Johnson, N.F., Jajodia, S.: Exploring steganography: Seeing the unseen. In: Computer, vol. 31, pp. 26–34. IEEE Computer Society, Los Alamitos (1998)Google Scholar
  17. 17.
    Dong, J., Tan, T.: Blind image steganalysis based on run-length histogram analysis. In: 2008 IEEE International Conference on Image Processing(ICIP 2008) (2008)Google Scholar
  18. 18.
    Galloway, M.M.: Texture analysis using gray level run lengths. In: Cornput. Graph. Image Proc., vol. 4, pp. 171–179 (1975)Google Scholar
  19. 19.
    Shi, Y.Q., Chen, C., Chen, W.: A natural image model approach to splicing detection. In: ACM Workshop on Multimedia and Security, ACM MMSEC 2007 (2007)Google Scholar
  20. 20.
    Kovesi, P.: Phase congruency: A low-level image invariant. Psych. Research 64, 136–148 (2000)CrossRefGoogle Scholar
  21. 21.
    Sobel, I., Feldman, G.: A 3x3 isotropic gradient operator for image processing. In: Duda, R., Hart, P. (eds.) Pattern Classification and Scene Analysis, pp. 271–272. John Wiley and Sons, Chichester (1973)Google Scholar
  22. 22.
    Arfken, G.: Mathematical methods for physicists, 3rd edn. Academic Press, Orlando (1985)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jing Dong
    • 1
  • Wei Wang
    • 1
  • Tieniu Tan
    • 1
  • Yun Q. Shi
    • 2
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijing
  2. 2.Dept. of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkUSA

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