A Novel Machine Learning Approach to Prevent Illegal Distribution of Screen Captured Videos

Capturing of videos from the television (TV) screens or from the theater screens by using the mobile cameras and its illegal distribution through video-sharing websites like YouTube, Dailymotion, Metacafe, etc. is a well-known challenge faced by the film industry. The video-sharing websites like YouTube does not encourage the illegal distribution of videos (without proper consent from the content owner). Currently, the YouTube has a facility to remove an illegally distributed video content from its video repository based on the request from the content owner. In general, the removal of an illegally distributed video may take a few days, hence during this period, the video may be downloaded by many of the people. The downloaded videos may be again distributed over the internet through different modes. This paper proposed a new technique which will classify a given video into normal video or screen captured video and it can be incorporated with video-sharing websites to prevent the illegal distribution of screen captured videos. The proposed scheme uses a support vector machine model which is trained using no-reference image quality measures. As far as our knowledge is concerned, there is no related work in this area.