Novel Blind Video Forgery Detection Using Markov Models on Motion Residue

  • Kesav Kancherla
  • Srinivas Mukkamala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7198)


In this paper we present a novel blind video forgery detection method by applying Markov models to motion in videos. Motion is an important aspect of video forgery detection as it effects forgery detection in videos. Most of the current video forgery detection algorithms do not consider motion in their approach. Motion is usually captured from motion vectors and prediction error frame. However capturing motion for I-frame is computationally expensive, so in this paper we extract the motion information by applying collusion on successive frames. First a base frame is obtained by applying collusion on successive frames and the difference between actual and estimate gives information about motion. Then we apply Markov models on this motion residue and apply pattern recognition on this. We used Support Vector Machines (SVMs) in our experiment. We obtained an accuracy of 87% even for reduced feature set.


Motion Vector Base Frame Forgery Attack Motion Picture Expert Group Apply Pattern Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, W., Farid, H.: Exposing digital forgeries in video by detecting double quantization. In: Proceedings of the 11th ACM workshop on Multimedia and Security - MM&Sec 2009, New York, NY (2009)Google Scholar
  2. 2.
    Hsu, C., Hung, T., Lin, C., Hsu, C.: Video forgery detection using correlation of noise residue. In: Proceedings of IEEE Workshop Multimedia Signal Processing (MMSP), Cairns, Queensland, Australia, pp. 170–174 (2008)Google Scholar
  3. 3.
    Kobayashi, M., Okabe, T., Sato, Y.: Detecting Forgery From Static-Scene Video Based on Inconsistency in Noise Level Functions. IEEE Transactions on Information Forensics and Security 5(4), 883–892 (2010)CrossRefGoogle Scholar
  4. 4.
    Mihcak, M.K., Kozintsev, I., Ramchandran, K.: Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising. In: Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, Phoenix, AZ, vol. 6, pp. 3253–3256 (1999)Google Scholar
  5. 5.
    Wang, W., Farid, H.: Exposing digital forgeries in interlaced and de-Interlaced Video. IEEE Transactions on Information Forensics and Security 2(3), 438–449 (2007)CrossRefGoogle Scholar
  6. 6.
    Wang, W., Farid, H.: Exposing digital forgeries in video by detecting duplication. In: Proceedings of the Multimedia and Security Workshop, Dallas, TX, pp. 35–42 (2007)Google Scholar
  7. 7.
    Zhang, J., Su, Y., Zhang, M.: Exposinig digital video forgery by ghost shadow artifact. In: Proc. of ACM Workshop on Multimedia in Forensics, Security and Intelligence, Beijing, China, pp. 49–53 (2009)Google Scholar
  8. 8.
  9. 9.
  10. 10.
    Video Motion Interpolation for Special Effect,
  11. 11.
    Video Inpainting Under Camera Motion,
  12. 12.
    Egan, J.P.: Signal detection theory and ROC analysis. Academic Press, New York (1975)Google Scholar
  13. 13.
    Zhao, H., Wu, M., Wang, Z., Liu, K.J.R.: Forensic Analysis of Nonlinear Collusion Attacks for Multimedia Fingerprinting. IEEE Transactions on Image Processing 14(5), 646–661 (2005)CrossRefGoogle Scholar
  14. 14.
    Lee, J.H., Lin, C.J.: Automatic model selection for support vector machines, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University (2000)Google Scholar
  15. 15.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines, Department of Computer Science and Information Engineering, National Taiwan University (2001)Google Scholar
  16. 16.
    Pevny, T., Fridrich, J.: Merging Markov and DCT features for multi-class JPEG steganalysis. In: Proceedings of SPIE Electronic Imaging, Photonics West, pp. 03–04 (2007)Google Scholar
  17. 17.
    Shi, Y.Q., Chen, C.-H., Chen, W.: A Markov Process Based Approach to Effective Attacking JPEG Steganography. In: Camenisch, J.L., Collberg, C.S., Johnson, N.F., Sallee, P. (eds.) IH 2006. LNCS, vol. 4437, pp. 249–264. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kesav Kancherla
    • 1
  • Srinivas Mukkamala
    • 1
  1. 1.Department of Computer Science Institute for Complex Additive Systems and Analysis (ICASA) Computational Analysis and Network Enterprise Solutuons (CAaNES)New Mexico Institute of Mining and TechnologySocorroU.S.A.

Personalised recommendations