Abstract
YouTube is one of the largest video sharing website on the Internet. Several music and record companies, artists and bands have official channels on YouTube (part of the music ecosystem of YouTube) to promote and monetize their music videos. YouTube consists of huge amount of copyright violated content including music videos (focus of the work presented in this paper) despite the fact that they have defined several policies and implemented measures to combat copyright violations of content. We present a method to automatically detect copyright violated videos by mining video as well as uploader meta-data. We propose a multi-step approach consisting of computing textual similarity between query video title and video search results, detecting useful linguistic markers (based on a pre-defined lexicon) in title and description, mining user profile data, analyzing the popularity of the uploader and the video to predict the category (original or copyright-violated) of the video. Our proposed solution approach is based on a rule-based classification framework. We validate our hypothesis by conducting a series of experiments on evaluation dataset acquired from YouTube. Empirical results indicate that the proposed approach is effective.
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References
Pike, G.H.: Legal Issues: Google YouTube Copyright and Privacy. Information Today 24(4), 15 (2007)
Library of Congress, How to Investigate the Copyright Status of a Work, United States copyright office, Washington, DC 20559 (January 1991)
Russ Versteeg Viacom V/S YouTube: Preliminary Observations North Carolina. Journal of Law & Technology 9(1) (Fall 2007)
Kim, E.C.: YouTube: Testing the Safe, Harbors Of Digital, Copyright Law 17 S. Cal. Interdisc. L.J. 139 (2007-2008)
Breen, J.C.: YouTube or YouLose? Can YouTube Survive a Copyright Infringement Lawsuits. UCLA School of Law Year, Texas Intellectual Property. Journal 16(1), 151–182 (2007), http://works.bepress.com/jasonbreen/1
Wang, A.L.-C.: An Industrial-Strength Audio Search Algorithm. In: Choudhury, S., Manus, S. (eds.) The International Society for Music Information Retrieval, 4th Symposium Conference on Music Information Retrieval, ISMIR 2003, pp. 7–13 (October 2003), http://www.ismir.net , http://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf
Siersdorfer, S., Pedro, J.S., Sanderson, M.: Automatic Video Tagging using Content Redundancy. In: 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 395–402 (July 2009)
Wu, X., Hauptmann, A.G., Ngo, C.-W.: Practical Elimination of Near-Duplicates from Web Video Search. In: Proceedings of the 15 International Conference on Multimedia, pp. 218–227. ACM, New York (2007)
Kim, H., Lee, J., Liu, H., Lee, D.: Video Linkage: Group Based Copied Video Detection. In: Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval, CIVR 2008, pp. 397–406 (July 2008)
Paisitkriangkrai, S., Mei, T., Zhang, J., Hua, X.-S.: Scalable Clip-based Near-duplicate Video Detection with Ordinal Measure. In: Proceedings of the ACM International Conference on Image and Video Retrieval, CIVR 2010, pp. 121–128 (2010)
Shen, J., Mei, T., Gao, X.: Automatic Video Archaeology: Tracing Your Online Videos. In: Proceedings of the Second ACM SIGMM Workshop on Social Media, WSM 2010, pp. 59–64 (2010)
Zhu, G., Yang, M., Yu, K., Xu, W., Gong, Y.: Detecting Video Events Based on Action Recognition in Complex Scenes Using Spatio-Temporal Descriptor. In: Proceedings of the 17th ACM International Conference on Multimedia, pp. 165–174 (October 2009)
Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A Comparison of String Distance Metrics for Name-Matching Tasks. In: Proceedings of IJCAI 2003 Workshop on Information Integration, pp. 73–78 (August 2003)
Law-to, J., Buisson, O., Chen, L., Ipswich, M.H., Gouet-brunet, V., Joly, A., Boujemaa, N., Laptev, I., Stentiford, F., Ipswich, M.H.: Video copy detection: a comparative study. In: CIVR, pp. 371–378 (2007)
Liu, L., Lai, W., Hua, X.-S., Yang, S.-Q.: On Real-Time Detecting Duplicate Web Videos. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2007, vol. 1, pp. 973–976 (2007) ISSN 1520-6149
Min, H.-S., Choi, J.Y., De Neve, W., Ro, Y.M.: Near-Duplicate Video Clip Detection Using Model-Free Semantic Concept Detection and Adaptive Semantic Distance Measurement. IEEE Transactions on Circuits and Systems for Video Technology 22(8), 1174–1187 (2012) ISSN 1051-8215
Hoi, C.-H., Wang, W., Lyu, M.R.: A Novel Scheme for Video Similarity Detection. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 373–382. Springer, Heidelberg (2003)
Wu, X., Ngo, C.-W., Hauptmann, A.G., Tan, H.-K.: Real-Time Near-Duplicate Elimination for Web Video Search With Content and Context. IEEE Transactions on Multimedia 11(2), 196–207 (2009) ISSN 1520-9210
Chaudhary, V., Sureka, A.: Contextual Feature Based One-Class Classier Approach for Detecting Video Response Spam on YouTube. In: Eleventh Annual Conference on Privacy, Security and Trust, PST (2013)
Jansohn, C., Ulges, A., Breuel, T.M.: Detecting pornographic video content by combining image features with motion information. In: Proceedings of the 17th ACM International Conference on Multimedia, MM 2009, pp. 601–604. ACM, New York (2009)
Fu, T., Huang, C.-N., Chen, H.: Identification of extremist videos in online video sharing sites. In: IEEE International Conference on Intelligence and Security Informatics, ISI 2009, pp. 179–181 (2009)
Sureka, A., Kumaraguru, P., Goyal, A., Chhabra, S.: Mining YouTube to Discover Extremist Videos, Users and Hidden Communities. In: Proceedings 6th Asia Information Retrieval Societies Conference, AIRS 2010, Taipei, Taiwan, December 1-3, pp. 13–24 (2010)
Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 693–696. Springer, Heidelberg (2013)
Educause Learning Initiatives, 7 things you should know about...YouTube (September 2006), http://www.educause.edu/library/resources/7-things-you-should-know-about-youtube
US copyright Office, The Digital Millennium Copyright Act of 1998, U.S. Copyright Office Summary (December 1998), http://www.copyright.gov/legislation/dmca.pdf
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Agrawal, S., Sureka, A. (2013). Copyright Infringement Detection of Music Videos on YouTube by Mining Video and Uploader Meta-data. In: Bhatnagar, V., Srinivasa, S. (eds) Big Data Analytics. BDA 2013. Lecture Notes in Computer Science, vol 8302. Springer, Cham. https://doi.org/10.1007/978-3-319-03689-2_4
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DOI: https://doi.org/10.1007/978-3-319-03689-2_4
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