A Distributed Scheme for Image Splicing Detection

  • Xudong Zhao
  • Shilin Wang
  • Shenghong Li
  • Jianhua Li
  • Xiang Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8389)

Abstract

In order to capture more splicing traces and to improve the robustness to anti-forensics, combining different kinds of features are adopted for image detection work in recently years. However, the combined features inevitably increase the feature dimensionality and the computational complexity. In this paper, we propose a distributed approach to reducing the computational complexity introduced by the high-dimensional features in image splicing detection. We introduce first-order noncausal model to the splicing detection work and give the distributed solution to this model. The noncausal model is split into several small tasks which are solved simultaneously by the distributed scheme. Experimental results over the public Columbia Image Splicing Detection Evaluation Dataset show that the distributed noncausal model could differentiate between splicing images and natural ones effectively.

Keywords

Image splicing detection Distributed scheme First-order noncausal model 

References

  1. 1.
  2. 2.
    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
  3. 3.
    Hsu, Y.-F., Chang, S.-F.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: Proceedings of International Conference on Multimedia and Expo, pp. 549–552 (2006)Google Scholar
  4. 4.
    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
  5. 5.
    Wang, W., Dong, J., Tan, T.: Effective image splicing detection based on image chroma. In: Proceedings of IEEE International Conference on Image Processing (ICIP09), pp. 1257–1260 (2009)Google Scholar
  6. 6.
    Wang, W., Dong, J., Tan, T.: Image tampering detection based on stationary distribution of Markov chain. In: Proceedings of IEEE International Conference on Image Processing (ICIP10), pp. 2101–2104 (2010)Google Scholar
  7. 7.
    He, Z., Lu, W., Sun, W., Huang, J.: Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn. 45(12), 4292–4299 (2012)CrossRefGoogle Scholar
  8. 8.
    Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)CrossRefGoogle Scholar
  9. 9.
    Kirchner, M., Fridrich, J.: On detection of median filtering in digital images. In: SPIE Proceedings of Media Forensics and Security II, pp. 754110-1–754110-12 (2010)Google Scholar
  10. 10.
    Balram, N., Moura, J.M.F.: Noncausal predictive image codec. IEEE Trans. Image Process. 5(8), 1229–1242 (1996)CrossRefGoogle Scholar
  11. 11.
    Ma, X., Schonfeld, S., Khokhar, A.A.: Video event classification and image segmentation based on noncausal multidimensional hidden Markov models. IEEE Trans. Image Process. 18(6), 1304–1313 (2009)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Farid, H., Lyu, S.: Higher-order wavelet statistics and their application to digital forensics. In: Proceedings of Computer Vision and Pattern Recognition Workshop (CVPR03), pp. 94–101 (2003)Google Scholar
  13. 13.
    Ng, T.T., Chang, S.F., Sun, Q.: A data set of authentic and spliced image blocks. Technical report, DVMM, Columbia University (2004). http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/photographers.htm
  14. 14.
    DVMM Laboratory of Columbia University: Columbia Image Splicing Detection Evaluation Dataset. http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/photographers.htm
  15. 15.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001). http://www.csie.ntu.edu.tw/cjlin/libsvm

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Xudong Zhao
    • 1
  • Shilin Wang
    • 2
  • Shenghong Li
    • 1
  • Jianhua Li
    • 1
  • Xiang Lin
    • 2
  1. 1.Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Information Security EngineeringShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

Personalised recommendations