Abstract
Duplicated sequence of frames in a video to cover up or replicate a scene is a video forgery. There are methods to authenticate video files, but embedding authentication information into videos requires extra hardware or software. It is possible to detect frame duplication forgery by carefully inspecting the content to discover high correlation among group of frames. A new frame duplication detection method based on Bag-of-Words (BoW) model is proposed in this paper. BoW is a model used in textual analysis first and image and video retrieval later by researchers. We used BoW to create visual words and build a dictionary from Scale Independent Feature Transform (SIFT) keypoints of frames in video. Frame features, i.e., visual word representations at keypoints, are used to detect sequence of duplicated parts in the video. The method computes thresholds depending on the content to improve both robustness and performance. The proposed method is tested on 31 test videos selected from Surrey University Library for Forensic Analysis (SULFA) and from various movies. Experimental results show a better detection performance and reduced run time compared to similar methods reported in the literature.
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References
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)
Wang, W., Farid, H.: Exposing digital forgeries in interlaced and deinterlaced video. IEEE Trans Inf Forensic Secur. 2(3), 438–449 (2007)
Luo, W.Q., Wu, M., Huang, J.W.: Mpeg recompression detection based on block artifacts. In: Proceedings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents, vol. 6819, p 68190X (2008)
Wang, W., Farid, H.: Exposing digital forgeries in video by detecting double quantization. In: Proceedings 11th ACM workshop on Multimedia and Security, pp. 39–48 (2009)
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) https://doi.org/10.1109/MMSP.2012.6343421
Aghamaleki, J.A., Behrad, A.: Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding. Image Commun 47(C):289–302 (2016)
Hsu, C., Hung, T., Lin, C.: Video forgery detection using correlation of noise residue. In: Proceeding 10th Workshop on IEEE Multimedia Signal Processing, pp. 170–174 (2008)
Kobayashi, M., Okabe, T., Sato, Y.: Detecting video forgeries based on noise characteristics. Lect Notes Comput Sci Adv Image Video Technol. 5414, 306–317 (2009)
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)
Yuting, S., Jing, Z.: Exposing digital video forgery by detecting motion-compensated edge artifact. In: International conference on computational intelligence and software engineering, pp. 1–4 (2009)
Li, L., Wang, X., Zhang, W., Yang, G., Hu, G.: Detecting removed object from video with stationary background. Digital Forensics and Watermarking. Springer, pp. 242–252 (2013)
Lin, C.S., Tsay, J.J.: A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis. Digital Investig. 11, 120–140 (2014)
Wang, W., Farid, H.: Exposing digital forgeries in video by detecting duplication. In: Proceedings of the 9th workshop on Multimedia and security, ACM, pp 35–42 (2007)
Chao, J., Jiang, X.H., Sun, T.F.: A Novel Video Inter-Frame Forgery Model Detection Scheme Based on Optical Flow Consistency. Digital Forensics and Watermarking, pp. 267–281, Springer, Berlin (2013)
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)
Zhang, Z., Hou, J., Ma, Q., Li, Z.: 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)
Zheng, L., Sun, T., Shi, Y.-Q.: Inter-frame video forgery detection based on block-wise brightness variance descriptor. In: Digital-Forensics and Watermarking, vol. 9023, pp. 18–30. Springer (2015)
Liu, Y., Huang, T.: Exposing video inter-frame forgery by zernike opponent chromaticity moments and coarseness analysis. Multimed Syst. 23(2), 1–16 (2016)
Singh, V.K., Pant, P., Tripathi, R.C.: Detection of frame duplication type of forgery in digital video using sub-block based features. In: Digital Forensics and Cybercrime, vol. 157, pp. 29–38. Springer (2015)
Yang, J.M., Huang, T.Q., Su, L.C.: Using similarity analysis to detect frame duplication forgery in videos. Multimed Tools Appl. 1–19 (2014)
Bosch, A., Muñoz, X., Martí, R.: Which is the best way to organize/classify images by content. Image Vis. Comput. 25(6), 778–791 (2007)
Chih-Fong, T.: Bag-of-words representation in image annotation: a review. ISRN Artif. Intell. (2012). https://doi.org/10.5402/2012/376804
Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV ‘03), pp. 1470–1477 (2003)
Ballan, L., Bertini, M., Del Bimbo, A., Serra, G.: Video event classification using bag of words and string kernels. Image Anal. Process. 170–178 (2009)
Yang, J., Jiang, Y.-G., Hauptmann, A.G., Ngo, C.-W.: Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval, pp. 197–206 (2007)
Mironică, I., Duţă, I., Ionescu, B., Sebe, N.: Beyond bag-of-words: fast video classification with fisher kernel vector of locally aggregated descriptors. In: IEEE International Conference on Multimedia and Expo (ICME), Turin, pp. 1–6 (2015)
Elshourbagy, M., Hemayed, E., Fayek, M.: Enhanced bag of words using multilevel k-means for human activity recognition. Egypt. Inf. J. 17, 227–237 (2016)
Shukla, P., Biswas, K.K., Kalra, P.K., Action recognition using temporal bag-of-words from depth maps. In: International Conference on Machine Vision Applications, pp. 41–44 (2013)
Iosifidis, A., Tefas, A., Pitas, I.: Merging linear discriminant analysis with Bag of Words model for human action recognition. In: IEEE International Conference on Image Processing (ICIP), Quebec City, QC, pp. 832–836 (2015)
Lowe, G.: SIFT—the scale invariant feature transform. Int J. Comput Vis. 2, 91–110 (2004)
VLFeat open source library. http://www.vlfeat.org/ (2015)
Visalakshi, N.K., Thangavel, K.: Impact of normalization in distributed k-means clustering. Int. J. Soft Comput. 4, 168–172 (2009)
K means ++. https://en.wikipedia.org/wiki/K-means%2B%2B (2015)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceeding ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035 (2007)
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This work is supported by Tubitak with Project Number 115E214.
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Communicated by J. Dittmann.
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Ulutas, G., Ustubioglu, B., Ulutas, M. et al. Frame duplication detection based on BoW model. Multimedia Systems 24, 549–567 (2018). https://doi.org/10.1007/s00530-017-0581-6
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DOI: https://doi.org/10.1007/s00530-017-0581-6