Detection of Image Splicing Based on Hilbert-Huang Transform and Moments of Characteristic Functions with Wavelet Decomposition

  • Dongdong Fu
  • Yun Q. Shi
  • Wei Su
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4283)


Image splicing is a commonly used technique in image tampering. This paper presents a novel approach to passive detection of image splicing. In the proposed scheme, the image splicing detection problem is tackled as a twoclass classification problem under the pattern recognition framework. Considering the high non-linearity and non-stationarity nature of image splicing operation, a recently developed Hilbert-Huang transform (HHT) is utilized to generate features for classification. Furthermore, a well established statistical natural image model based on moments of characteristic functions with wavelet decomposition is employed to distinguish the spliced images from the authentic images. We use support vector machine (SVM) as the classifier. The initial experimental results demonstrate that the proposed scheme outperforms the prior arts.


image splicing Hilbert-Huang transform (HHT) characteristic functions support vector machine (SVM) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Popescu, A.C.: Statistical tools for digital image forensics. Ph.D. Dissertation, Department of Computer Science, Dartmouth College (2005)Google Scholar
  2. 2.
    Farid, H.: Detection digital forgeries using bispectral analysis. Technical Report, AIM-1657, MIT AI Memo (1999)Google Scholar
  3. 3.
    Ng, T.-T., Chang, S.-F.: Blind detection of photomontage using higher order statistics. ADVENT Technical Report #201-2004-1, Columbia University (June 2004)Google Scholar
  4. 4.
    Ng, T.-T., Chang, S.-F., Sun, Q.: Blind detection of photomontage using higher order statistics. In: IEEE International Symposium on Circuits and Systems (ISCAS), Vancouver, Canada (May 2004)Google Scholar
  5. 5.
    Columbia DVMM Research Lab: Columbia Image Splicing Detection Evaluation Dataset (2004),
  6. 6.
    Huang, N.E., Shen, Z., Long, S.R.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A(454), 903–995 (1998)MathSciNetGoogle Scholar
  7. 7.
  8. 8.
    Yang, Z., Qi, D., Yang, L.: Signal period analysis based on Hilbert-Huang transform and its application to texture analysis. In: International Conference of Image and Graphic, Hong Kong (2004)Google Scholar
  9. 9.
    Farid, H., Lyu, S.: Higher-order wavelet statistics and their application to digital forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision, Madison, Wisconsin (2003)Google Scholar
  10. 10.
    Shi, Y.Q., Xuan, G., Zou, D., Gao, J., Yang, C., Zhang, Z., Chai, P., Chen, W., Chen, C.: Steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network. In: International Conference on Multimedia and Expo., Amsterdam, Netherlands (2005)Google Scholar
  11. 11.
    Leon-Garcia, A.: Probability and Random Processes for Electrical Engineering, 2nd edn. Addison-Wesley Publishing Company, Reading (1994)Google Scholar
  12. 12.
    Weinberger, M., Seroussi, G., Sapiro, G.: LOCOI: A low complexity context-based lossless image compression algorithm. In: Proceeding of IEEE Data Compression Conference, pp. 140–149 (1996)Google Scholar
  13. 13.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dongdong Fu
    • 1
  • Yun Q. Shi
    • 1
  • Wei Su
    • 1
  1. 1.Dept. of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkUSA

Personalised recommendations