Abstract
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.
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References
Wang, W.H., Farid, H.: Exposing digital forgeries in video by detecting duplication. In: MM & Sec 2007, pp. 35–42 (2007)
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)
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)
Yahaya, S., Ho, A.T.S., Li, S.J.: Improving Conditional Probability Based Camera Source Identification
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Iglewicz, B., Hoaglin, D.C.: How to Detect and Handle Outliers, vol. 16. ASQC Quality Press, Milwaukee (1993)
Acknowledgement
We would like to thank Yuxing Wu and Dr. Tanfeng Sun from Shanghai Jiao Tong University for their kindness by providing us with their database and codes. We also appreciate Guanshuo Xu for his help and kindly suggestions. The first author was supported by the Fundamental Research Funds for the Central Universities (W13JB00070) and (W15JB00280), and SRF for ROCS, SEM (W15C300020).
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Zhang, Z., Hou, J., Li, Z., Li, D. (2016). Inter-frame Forgery Detection for Static-Background Video Based on MVP Consistency. In: Shi, YQ., Kim, H., Pérez-González, F., Echizen, I. (eds) Digital-Forensics and Watermarking. IWDW 2015. Lecture Notes in Computer Science(), vol 9569. Springer, Cham. https://doi.org/10.1007/978-3-319-31960-5_9
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DOI: https://doi.org/10.1007/978-3-319-31960-5_9
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