Video Inter-Frame Forgery Identification Based on Consistency of Correlation Coefficients of Gray Values

Abstract

Identifying inter-frame forgery is a hot topic in video forensics. In this paper, we propose a method based on the assumption that the correlation coefficients of gray values is consistent in an original video, while in forgeries the consistency will be destroyed. We first extract the consistency of correlation coefficients of gray values (CCCoGV for short) after normalization and quantization as distinguishing feature to identify interframe forgeries. Then we test the CCCoGV in a large database with the help of SVM (Support Vector Machine). Experimental results show that the proposed method is efficient in classifying original videos and forgeries. Furthermore, the proposed method performs also pretty well in classifying frame insertion and frame deletion forgeries.

Share and Cite:

Wang, Q. , Li, Z. , Zhang, Z. and Ma, Q. (2014) Video Inter-Frame Forgery Identification Based on Consistency of Correlation Coefficients of Gray Values. Journal of Computer and Communications, 2, 51-57. doi: 10.4236/jcc.2014.24008.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Hsu, C.C., Hung, T.Y., Lin, C.W. and Hsu, C.T. (2008) Video Forgery Detection Using Correlation of Noise Residue. 2008 IEEE 10th Workshop on Multimedia Signal Processing.
[2] Li, L.D., Wang, X.W., Zhang, W., Yang, G.B. and Hu, G.Z. (2013) Detecting Removed Object from Video with Stationary Background. Digital Forensics and Water-marking. Lecture Notes in Computer Science, 7809, 242-252.
[3] Subramanyam, A.V. and Emmanuel, S. (2012) Video Forgery Detection Using HOG Features and Compression Properties. 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), 89-94.
[4] Chao, J., Jiang, X.H. and Sun, T.F. (2013) A Novel Video Inter-Frame Forgery Model Detection Scheme Based on Optical Flow Consistency. Digital Forensics and Watermarking, Springer, Berlin, Heidelberg, 267-281.
[5] Huang, TQ., Chen, Z.W., Su, L.C., Zheng, Z. and Yuan, X.J. (2011) Digital Video Forgeries Detection Based on Content Continuity. Journal of Nanjing University (Natural Science), 47, 493-503.
[6] Chang, C.-C. and Lin, C.-J. (2011) LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology (TIST), 27, 1-27. http://www.csie.ntu.edu.tw/~cjlin/libsvm/

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