Steganalysis Versus Splicing Detection

  • Yun Q. Shi
  • Chunhua Chen
  • Guorong Xuan
  • Wei Su
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5041)


Aiming at detecting secret information hidden in a given image using steganographic tools, steganalysis has been of interest for years. In particular, universal steganalysis, not limited to attacking a specific steganographic tool, is of extensive interests due to its practicality. Recently, splicing detection, another important area in digital forensics has attracted increasing attention. Is there any relationship between steganalysis and splicing detection? Is it possible to apply universal steganalysis methodologies to splicing detection? In this paper, we address these intact and yet interesting questions. Our analysis and experiments have demonstrated that, on the one hand, steganography and splicing have different goals and strategies, hence, generally causing different statistical artifacts on images. However, on the other hand, both of them make the touched (stego or spliced) image different from the corresponding original (natural) image. Therefore, natural image model based on a set of carefully selected statistical features under the machine learning framework can be used for steganalysis and splicing detection. It is shown in this paper that some successful universal steganalytic schemes can make promising progress in splicing detection if applied properly. A more advanced natural image model developed from these state-of-the-art steganalysis methods is thereafter presented. Furthermore, a concrete implementation of the proposed model is applied to the Columbia Image Splicing Detection Evaluation Dataset, which has achieved an accuracy of 92%, indicating a significant advancement in splicing detection.


Steganography steganalysis splicing detection tampering detection digital forensics natural image model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kharrazi, M., Sencar, H.T., Memon, N.: Image Steganography: Concepts and Practice. In: Lecture Note Series, Institute for Mathematical Sciences, National University of Singapore (2004)Google Scholar
  2. 2.
  3. 3.
    Columbia DVMM Research Lab: Columbia Image Splicing Detection Evaluation Dataset (2004),
  4. 4.
  5. 5.
    Lyu, S., Farid, H.: Detecting Hidden Messages Using Higher-order Statistics and Support Vector Machines. In: Information Hiding Workshop, Noordwijkerhout, Netherlands (2002)Google Scholar
  6. 6.
    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
  7. 7.
    Zou, D., Shi, Y.Q., Su, W., Xuan, G.: Steganalysis Based on Markov Model of Thresholded Prediction-Error Image. In: International Conference on Multimedia and Expo, Toronto, ON, Canada (2006)Google Scholar
  8. 8.
    Shi, Y.Q., Chen, C., Chen, W.: A Markov Process Based Approach to Effective Attacking JEPG Steganography. In: Information Hiding Workshop, Old Town Alexandria, VA, USA (2006)Google Scholar
  9. 9.
    Chen, C., Shi, Y.Q., Xuan, G.: Steganalyzing Texture images. In: International Conference on Image Processing, St. Antonio, TX, USA (2007)Google Scholar
  10. 10.
    Fridrich, J.: Feature-based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes. In: Information Hiding Workshop, Toronto, ON, Canada (2004)Google Scholar
  11. 11.
    Ng, T.-T., Chang, S.-F., Sun, Q.: Blind Detection of Photomontage Using Higher Order Statistics. In: IEEE International Symposium on Circuits and Systems, Vancouver, BC, Canada (2004)Google Scholar
  12. 12.
    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.) Digital Watermarking, Proceeding of 5th International Workshop on Digital Watermarking, Jeju Island, Korea (2006)Google Scholar
  13. 13.
    Chen, W., Shi, Y.Q., Su, W.: Image Splicing Detection Using 2-D Phase Congruency and Statistical Moments of Characteristic Function. In: Delp, E. J., Wong, P.W. (eds.) Security, Steganography and Watermarking of Multimedia Contents IX, Proceeding. of SPIE, San Jose, CA, USA (2007)Google Scholar
  14. 14.
    Farid, H.: A Picture Tells a Thousand Lies. New Scientist 179(2411), 38–41 (2003)Google Scholar
  15. 15.
    Ng, T.-T., Chang, S.-F.: A Model for Image Splicing. In: IEEE International Conference on Image Processing, Singapore (2004)Google Scholar
  16. 16.
    Bayram, S., Avcibas, I., Sankur, B., Memon, N.: Image Manipulation Detection. Journal of Electronic Imaging 15(4) (2006)Google Scholar
  17. 17.
    Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines,
  18. 18.
    Fawcett, T.: Roc Graphs: Notes and Practical Considerations for Researchers,
  19. 19.
    Leon-Garcia, A.: Probability and Random Processes for Electrical Engineering, 2nd edn. Addison-Wesley Publishing Company, Reading (1994)zbMATHGoogle Scholar
  20. 20.
    Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  21. 21.
    Shi, Y.Q., Sun, H.: Image and Video Compression for Multimedia Engineering: Fundamentals, Algorithms, and Standards. CRC Press, Boca Raton (1999)CrossRefGoogle Scholar
  22. 22.
    Forchheimer, R., Kronander, T.: Image Coding from Waveforms to Animation. IEEE Transactions on Acoustics, Speech and Signal Processing 37(12), 2008–2023 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yun Q. Shi
    • 1
  • Chunhua Chen
    • 1
  • Guorong Xuan
    • 2
  • Wei Su
    • 1
  1. 1.New Jersey Institute of TechnologyNewarkUSA
  2. 2.Tongji UniversityShanghaiChina

Personalised recommendations