Inter-frame Forgery Detection for Static-Background Video Based on MVP Consistency

  • Zhenzhen Zhang
  • Jianjun Hou
  • Zhaohong Li
  • Dongdong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9569)


Frame deletion and duplication are common inter-frame tampering methods in digital videos. In this paper, an efficient forensic method based on motion vector pyramid (MVP) and its variation factor (VF) is proposed to detect frame deletion and duplication in videos with static background. This method is composed of two parts: feature extraction and discontinuity point detection. In the stage of feature extraction, each frame of the video is transformed to grayscale image firstly. Then, motion vector pyramid (MVP) sequence and its corresponding variation factor (VF) are calculated for every two adjacent frames. In the stage of discontinuity point detection, forgery type is identified and tampering point is localized by performing modified generalized ESD test. Experimental results show that the proposed method is efficient at forgery identification and localization. Compared with other existing methods on inter-frame forgery detection, our proposed method is more generic.


Video forensics Inter-frame forgery Motion vector Image pyramid Static background 


  1. 1.
  2. 2.
  3. 3.
    Wang, W.H., Farid, H.: Exposing digital forgeries in video by detecting duplication. In: MM & Sec 2007, pp. 35–42 (2007)Google Scholar
  4. 4.
    Chen, M., Fridrich, J., Goljan, M., Luk\(\grave{a}\breve{s}\), J.: Source digital camcorder identification using sensor photo response non-uniformity. In: Electronic Imaging 2007, International Society for Optics and Photonics (2007)Google Scholar
  5. 5.
    Yahaya, S., Ho, A.T.S., Wahab, A.A.: Advanced video camera identification using conditional probability features. In: IET Conference on Image Processing, pp. 1–5 (2012)Google Scholar
  6. 6.
    Yahaya, S., Ho, A.T.S., Li, S.J.: Improving Conditional Probability Based Camera Source IdentificationGoogle Scholar
  7. 7.
    Cheng-Shian, L., Jyh-Jong, T.: A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis. Digital Invest. Int. J. Digit. Forensics Incident Response 11(2), 120–140 (2014)Google Scholar
  8. 8.
    Kobayashi, M., Okabe, T., Sato, Y.: Detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Trans. Inf. Forensics Secur. 5(4), 883–892 (2010)CrossRefGoogle Scholar
  9. 9.
    Chen, W., Shi, Y.Q.: Detection of double mpeg compression based on first digit statistics. In: Kim, H.-J., Katzenbeisser, S., Ho, A.T.S. (eds.) IWDW 2008. LNCS, vol. 5450, pp. 16–30. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Milani, S., Bestagini, P., Tagliasacchi, M., Tubaro, S.: Multiple compression detection for video sequences. In: 2012 IEEE 14th International Workshop on Multimedia Signal Processing, pp. 112–117 (2012)Google Scholar
  11. 11.
    Wang, W.H., Farid, H.: Exposing digital forgeries in video by detecting double MPEG compression. In: Proceedings of the 8th ACM Workshop on Multimedia and Security, pp. 37–47 (2006)Google Scholar
  12. 12.
    Stamm, M.C., Lin, W.S., Liu, K.J.R.: Temporal forensics and anti-forensics for motion compensated video. IEEE Trans. Inf. Forensics Secur. 7(4), 1315–1329 (2012)CrossRefGoogle Scholar
  13. 13.
    Gironi, A., Fontani, M., Bianchi, T., Piva, A., Barni, M.: A video forensic technique for detecting frame deletion and insertion. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6226–6230 (2014)Google Scholar
  14. 14.
    Chao, J., Jiang, X., Sun, T.: A novel video inter-frame forgery model detection scheme based on optical flow consistency. In: Shi, Y.Q., Kim, H.-J., Pérez-González, F. (eds.) IWDW 2012. LNCS, vol. 7809, pp. 267–281. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Zhang, Z.Z., Hou, J.J., Ma, Q.L., Li, Z.H.: Efficient video frame insertion and deletion detection based on inconsistency of correlations between local binary pattern coded frames. Secur. Commun. Netw. 8(2), 311–320 (2015)CrossRefGoogle Scholar
  16. 16.
    Wu, Y.X., Jiang, X.H., Sun, T.F., Wang, W.: Exposing video inter-frame forgery based on velocity field consistency. In: 2014 IEEE International Conference on, Acoustics, Speech and Signal Processing (ICASSP), pp. 2674–2678 (2014)Google Scholar
  17. 17.
    Wang, W., Jiang, X., Wang, S., Wan, M., Sun, T.: Identifying Video Forgery Process Using Optical Flow. In: Shi, Y.Q., Kim, H.-J., Pérez-González, F. (eds.) IWDW 2013. LNCS, vol. 8389, pp. 244–257. Springer, Heidelberg (2014)Google Scholar
  18. 18.
    Iglewicz, B., Hoaglin, D.C.: How to Detect and Handle Outliers, vol. 16. ASQC Quality Press, Milwaukee (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhenzhen Zhang
    • 1
  • Jianjun Hou
    • 1
  • Zhaohong Li
    • 1
  • Dongdong Li
    • 1
  1. 1.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina

Personalised recommendations