Copyright Infringement Detection of Music Videos on YouTube by Mining Video and Uploader Meta-data

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.

[1]  J. Breen,et al.  YouTube or YouLose? Can YouTube Survive a Copyright Infringement Lawsuit , 2007 .

[2]  Adrian Ulges,et al.  Detecting pornographic video content by combining image features with motion information , 2009, ACM Multimedia.

[3]  Mohan S. Kankanhalli,et al.  Proceedings of the 2008 international conference on Content-based image and video retrieval , 2008 .

[4]  Michael R. Lyu,et al.  A Novel Scheme for Video Similarity Detection , 2003, CIVR.

[5]  Ashish Sureka,et al.  Contextual feature based one-class classifier approach for detecting video response spam on YouTube , 2013, 2013 Eleventh Annual Conference on Privacy, Security and Trust.

[6]  Tao Mei,et al.  Automatic video archaeology: tracing your online videos , 2010, WSM '10.

[7]  Ming Yang,et al.  Detecting video events based on action recognition in complex scenes using spatio-temporal descriptor , 2009, ACM Multimedia.

[8]  Li Chen,et al.  Video copy detection: a comparative study , 2007, CIVR '07.

[9]  Hung-Khoon Tan,et al.  Real-Time Near-Duplicate Elimination for Web Video Search With Content and Context , 2009, IEEE Transactions on Multimedia.

[10]  Yiannis Kompatsiaris,et al.  Proceedings of the ACM International Conference on Image and Video Retrieval , 2009, CIVR 2009.

[11]  Chong-Wah Ngo,et al.  Practical elimination of near-duplicates from web video search , 2007, ACM Multimedia.

[12]  Wesley De Neve,et al.  Near-Duplicate Video Clip Detection Using Model-Free Semantic Concept Detection and Adaptive Semantic Distance Measurement , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Haibin Liu,et al.  Video linkage: group based copied video detection , 2008, CIVR '08.

[14]  Peter Ingwersen,et al.  Developing a Test Collection for the Evaluation of Integrated Search , 2010, ECIR.

[15]  Avery Wang,et al.  An Industrial Strength Audio Search Algorithm , 2003, ISMIR.

[16]  Lu Liu,et al.  On Real-Time Detecting Duplicate Web Videos , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[17]  Hsinchun Chen,et al.  Identification of extremist videos in online video sharing sites , 2009, 2009 IEEE International Conference on Intelligence and Security Informatics.

[18]  Wei-Ying Ma,et al.  Image and Video Retrieval , 2003, Lecture Notes in Computer Science.

[19]  Pradeep Ravikumar,et al.  A Comparison of String Distance Metrics for Name-Matching Tasks , 2003, IIWeb.

[20]  Educause 7 Things You Should Know About...Screencasting , 2006 .

[21]  Dolf Trieschnigg,et al.  Improving Cyberbullying Detection with User Context , 2013, ECIR.

[22]  Ponnurangam Kumaraguru,et al.  Mining YouTube to Discover Extremist Videos, Users and Hidden Communities , 2010, AIRS.

[23]  Tao Mei,et al.  Scalable clip-based near-duplicate video detection with ordinal measure , 2010, CIVR '10.

[24]  Mark Sanderson,et al.  Automatic video tagging using content redundancy , 2009, SIGIR.