Image Splicing Detection Based on Markov Features in QDCT Domain

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9226)


Image splicing is very common and fundamental in image tampering. Therefore, image splicing detection has attracted more and more attention recently in digital forensics. Gray images are used directly, or color images are converted to gray images before processing in previous image splicing detection algorithms. However, most natural images are color images. In order to make use of the color information in images, a classification algorithm is put forward which can use color images directly. In this paper, an algorithm based on Markov in Quaternion discrete cosine transform (QDCT) domain is proposed for image splicing detection. The support vector machine (SVM) is exploited to classify the authentic and spliced images. The experiment results demonstrate that the proposed algorithm not only make use of color information of images, but also can achieve high classification accuracy.


Markov model QDCT Image-splicing detection Color image forgery detection 


  1. 1.
    Shi, Y.Q., Chen, C., Chen, W.: A natural image model approach to splicing detection. In: ACM Proceedings of the 9th Workshop on Multimedia and Security, pp. 51–62 (2007)Google Scholar
  2. 2.
    Ng, T.T., Chang, S.F., Sun, Q.: A data set of authentic and spliced image blocks. Columbia Univ. 203 (2004)Google Scholar
  3. 3.
    Wang, W., Dong, J., Tan, T.: Effective image splicing detection based on image chroma. In: IEEE International Conference on Image Processing, pp. 1257–1260 (2009)Google Scholar
  4. 4.
    Wang, W., Dong, J., Tan, T.: Image tampering detection based on stationary distribution of Markov chain. In: IEEE International Conference on Image Processing, pp. 2101–2104 (2010)Google Scholar
  5. 5.
  6. 6.
    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.) Transactions on Data Hiding and Multimedia Security VI. LNCS, vol. 6730, pp. 1–17. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Zhao, X., et al.: Passive image-splicing detection by a 2-D noncausal Markov model. IEEE Trans. Circ. Syst. Video Technol. 2(25), 185–199 (2014)Google Scholar
  8. 8.
    He, Z., Wei, L., Sun, W., et al.: Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn. 45(12), 4292–4299 (2012)CrossRefGoogle Scholar
  9. 9.
    Amerini, I., Becarelli, R., et al.: Splicing forgeries localization through the use of first digit features. In: IEEE Conference on Parallel Computing Technologies, pp. 143–148 (2015)Google Scholar
  10. 10.
    Feng, W., Hu, B.: Quaternion discrete cosine transform and its application in color template matching. IEEE Congr. Image Sig. Process. 5(2), 252–256 (2008)CrossRefGoogle Scholar
  11. 11.
    Schauerte, B., Stiefelhagen, R.: Quaternion-based spectral saliency detection for eye fixation prediction. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 116–129. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Chapelle, O.: Training a support vetor machine in the primal. Neural Comput. 19(5), 1155–1178 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ce Li
    • 1
    • 2
  • Qiang Ma
    • 1
  • Limei Xiao
    • 1
  • Ming Li
    • 1
  • Aihua Zhang
    • 1
  1. 1.College of Electrical and Information EngineeringLanzhou University of TechnologyLanzhouChina
  2. 2.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

Personalised recommendations