Multimedia Systems

, Volume 23, Issue 2, pp 223–238 | Cite as

Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis

Regular Paper

Abstract

Inter-frame forgery is the most common type of video forgery methods. However, few algorithms have been suggested for detecting this type of forgery, and the former detection methods cannot ensure the detection speed and accuracy at the same time. In this paper, we put forward a novel video forgery detection algorithm for detecting an inter-frame forgery based on Zernike opponent chromaticity moments and a coarseness feature analysis by matching from the coarse-to-fine models. Coarse detection applied to extract abnormal points is carried out first; each frame is converted from a 3D RGB color space into a 2D opposite chromaticity space combined with the Zernike moment correlation. The juggled points are then obtained exactly from abnormal points using a Tamura coarse feature analysis for fine detection. Coarse detection not only has a high-efficiency detection speed, but also a low omission ratio; however, it is accompanied by mistaken identifications, and the precision is not ideal. Therefore, fine detection was proposed to help to make up the difference in precision. The experimental results prove that this algorithm has a higher efficiency and accuracy than previous algorithms.

Keywords

Video passive forensics Inter-frame forgery Coarse–fine detection Opposite chromaticity space Zernike moment Coarseness 

References

  1. 1.
    Chen, W., Yang, G., Chen, R., Zhu, N.: Digital video passive forensics for its authenticity and source. J. Commun. 32(6), 77–182 (2011)Google Scholar
  2. 2.
    Zhou, L., Wang, D.: Digital image forensics, pp. 8–13. Beijing University of Posts and Telecommunications Press, Beijing (2008)Google Scholar
  3. 3.
    Wang, W., Farid, H.: Exposing digital forgeries in video by detecting double MPEG compression. In: Proceedings of the 8th Workshop on Multimedia and Security. ACM, pp. 37–47 (2006)Google Scholar
  4. 4.
    Wang, W., Farid, H.: Exposing digital forgeries in interlaced and deinterlaced video. IEEE Trans. Inf. Forensics Secur. 2(3), 438–449 (2007)CrossRefGoogle Scholar
  5. 5.
    Wang, W., Farid, H.: Exposing digital forgeries in video by detecting duplication. In: Proceedings of the 9th Workshop on Multimedia & security. ACM, pp. 35–42 (2007)Google Scholar
  6. 6.
    Wang, W., Farid, H.: Exposing digital forgeries in video by detecting double quantization. In: Proceedings of the 11th ACM Workshop on Multimedia and Security. ACM, pp. 39–48 (2009)Google Scholar
  7. 7.
    Su, Y., Zhang, J., Liu, J.: Exposing digital video forgery by detecting motion-compensated edge artifact. In: IEEE International Conference on Computational Intelligence and Software Engineering, pp. 1–4 (2009)Google Scholar
  8. 8.
    Qin, Y., Sun, G., Zhang, X.: Exposing digital forgeries in video via motion vectors. J. Comput. Res. Dev. 46, 227–233 (2009)Google Scholar
  9. 9.
    Huang, T., Chen, Z.: Digital video forgeries detection based on bidirectional motion vectors. J. Shandong Univ. (Engineering Science) 41(4), 13–19 (2011)Google Scholar
  10. 10.
    Hsu, C.C., Hung, T.Y., Lin, C.W.: Video forgery detection using correlation of noise residue. In: 2008 IEEE 10th Workshop on Multimedia Signal Processing, pp. 170–174 (2008)Google Scholar
  11. 11.
    Xiao, X., Zheng, H., Che, X.U.: Digital video forgeries detection based on prediction error. Inform. Secur. Commun. Priv. 12, 128–130 (2008)Google Scholar
  12. 12.
    Wang, J., Liu, G.: Detection of forgery in digital video based on pattern noise. J. Southeast Univ. (Natural Science Edition) S2, 13–17 (2008)Google Scholar
  13. 13.
    Yuan, X., Huang, T., Cheng, Z.: Digital video forgeries detection based on textural features. Comput. Syst. Appl. 21(6), 91–95 (2012)Google Scholar
  14. 14.
    Zhang, J., Su, Y., Zhang, M.: Exposing digital video forgery by ghost shadow artifact. In: Proceedings of the First ACM Workshop on Multimedia in Forensics. ACM, pp. 49–54 (2009)Google Scholar
  15. 15.
    Subramanyam, A.V., Emmanuel, S.: Video forgery detection using HOG features and compression properties. In: IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), pp. 89–94 (2012)Google Scholar
  16. 16.
    Huang, T., Cheng, Z., Su, L.: Digital video forgeries detection based on content continuity. J. Nanjing Univ. (Natural Sciences) 47(5), 493–503 (2011)Google Scholar
  17. 17.
    Cheng, Z., Huang, T., Wu, T.: Detection and recovery for copy-move forgery in homologous video. Comput. Syst. Appl. 22(9), 106–110 (2013)Google Scholar
  18. 18.
    Chao, J., Jiang, X., Sun, T.: A Novel Video Inter-Frame Forgery Model Detection Scheme Based on Optical Flow Consistency, Digital Forensics and Watermarking, pp. 267–281. Springer, Berlin/Heidelberg (2013)Google Scholar
  19. 19.
    Wang, Q., Li, Z., Zhang, Z.: Video inter-frame forgery identification based on consistency of correlation coefficients of gray values. J. Comput. Commun. 2(04), 51–57 (2014)CrossRefGoogle Scholar
  20. 20.
    Wu, T., Huang, T.: Video tamper detection based on inverse gravity density semi-supervised learning. Comput. Syst. Appl. 22(8), 91–102 (2013)Google Scholar
  21. 21.
    Li, F., Huang, T.: Video copy-move forgery detection and localization based on structural similarity. In: Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013), pp. 63–76 (2014)Google Scholar
  22. 22.
    Lin, G.S., Chang, J.F.: Detection of frame duplication forgery in videos based on spatial and temporal analysis. Int. J. Pattern Recognit. Artif. Intell. 26(07), 1–18 (2012)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Yap, P.T., Paramesran, R.: Content-based image retrieval using Legendre chromaticity distribution moments. IEE Proc. Vis. Image Signal Processing 153(1), 17–24 (2006)CrossRefGoogle Scholar
  24. 24.
    Flusser, J., Zitova, B., Suk, T.: Moments and Moment Invariants in Pattern Recognition. Wiley, Chichester (2009)CrossRefMATHGoogle Scholar
  25. 25.
    Qin, H., Qin, L., Xue, L.: A parallel recurrence method for the fast computation of Zernike moments. Appl. Math. Comput. 219(4), 1549–1561 (2012)MathSciNetMATHGoogle Scholar
  26. 26.
    Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 8(6), 460–473 (1978)CrossRefGoogle Scholar
  27. 27.
    Zhang, X., Shen, P., Gao, J.: A license plate recognition system based on Tamura texture in complex conditions. In: IEEE International Conference on Information and Automation (ICIA), pp. 1947–1952 (2010)Google Scholar
  28. 28.
    Majtner, T., Svoboda, D., Extension of Tamura texture features for 3D fluorescence microscopy. In: 2012 IEEE Second International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), pp. 301–307 (2012)Google Scholar
  29. 29.

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.College of information engineeringLongyan UniverstiyLongyanChina
  2. 2.Faculty of SoftwareFujian Normal UniversityFuzhouChina

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