Abstract
In the Human life, videos popularity has increased day by day. With the help of high qualityvideo camera and video editing tools peoples easily manipulate video for malicious intent. Sonow a day, this is becoming significant topic in the recent year with more difficulties. The proposed of research work focus in texture features by using Local Binary Pattern (LBP). The object of this work is to detect video inconsistency. The work is addressing in various steps as sequentially such as preprocessing, feature extraction, matching and decision. The evaluation of propose system by using True positive Rate (TPR) and False Positive Rate (FPR). The experimental results were executives on famous dataset REWIND database, which has 20 videos which give efficiency results with evaluation parameter TPR with \(0.0258\%\), FPR with \(0.0786\%\) and Area Under Curve (AUC) = \(0.7903\%\).
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References
Sowmya, K.N., Chennamma, H.R.: A survey on video forgery detection. Int. J. Comput. Eng. Appl. 9(2), 17–27 (2015). http://www.ijcea.com/wp-content/uploads/2015/03/02-Sowmya-K.N.pdf
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). https://doi.org/10.1109/TIFS.2010.2074194
Wang, W., Farid, H.: Exposing digital forgeries in video by detecting duplication. In: Proceedings of Workshop on Multimedia & Security International Multimedia Conference, New York, NY, pp. 35–42 (2007). https://doi.org/10.1145/1288869.1288876
https://sites.google.com/site/rewindpolimi/downloads/datasets/video-copy-move-forgeries-dataset
Labartinoet, D., et al.: Localization of forgeries in MPEG-2 video through GOP size and DQ analysis. In: MMSP 2013, Pula (Sardinia), Italy (2013)
Yongjian, H.: An improved fingerprinting algorithm for detection of video frame duplication forgery. Int. J. Digit. Crime Forensics 4(3), 20–32 (2012). https://doi.org/10.4018/jdcf.2012070102
Hsu, C.C., et al.: Video forgery detection using correlation of noise residue. In: IEEE International Workshop on Multimedia Signal Processing, Cairns, Australia, pp. 170–174 (2008). https://doi.org/10.1109/MMSP.2008.4665069
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognit. 29(1), 51–59 (1996). https://doi.org/10.1016/0031-3203(95)00067-4
Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Local binary patterns for still images. In: Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T. (eds.) Computer Vision Using Local Binary Patterns, pp. 13–47. Springer, London (2011). https://doi.org/10.1007/978-0-85729-748-8_2
Mahale, V.H., Ali, M.M.H., Yannawar, P.L., Gaikwad, A.T.: Image inconsistency detection using local binary pattern (LBP). Procedia Comput. Sci. 115, 501–508 (2017). https://doi.org/10.1016/j.procs.2017.09.097
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Gaikwad, A., Mahale, V., Ali, M.M.H., Yannawar, P.L. (2019). Detection and Analysis of Video Inconsistency Based on Local Binary Pattern (LBP). In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_9
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DOI: https://doi.org/10.1007/978-981-13-9181-1_9
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