Crowdsourcing-Based Copyright Infringement Detection in Live Video Streams

Abstract

With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes copyright has emerged as a new critical problem in online social media. In contrast to the traditional copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: one in which the content of interest is from live videos. We found that the state-of-the-art commercial copyright infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: large amounts of copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the copyright infringement detection problem for live videos, we identify several critical challenges: i) live streams are generated in real-time and the original copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and copyright-infringing ones. In this paper, we develop a crowdsourcing-based copyright infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting copyright-infringing live videos on YouTube.