Caught Red-Handed: Toward Practical Video-Based Subsequences Matching in the Presence of Real-World Transformations

Every minute, staggering amounts of user-generated videos are uploaded to on-line social networks. These videos can generate significant advertising revenue, providing strong incentive for unscrupulous individuals that wish to capitalize on this bonanza by pirating short clips from popular content and altering the copied media in ways that might bypass detection. Unfortunately, while the challenges posed by the use of skillful transformations has been known for quite some time, current state-of-the-art methods still suffer from severe limitations. Indeed, most of today's techniques perform poorly in the face of real world copies. To address this, we propose a novel approach that leverages temporal characteristics to identify subsequences of a video that were copied from elsewhere. Our approach takes advantage of a new temporal feature to index a reference library in a manner that is robust to popular spatial and temporal transformations in pirated videos. Our experimental evaluation on 27 hours of video obtained from social networks demonstrates that our technique significantly outperforms the existing state-of-the-art approaches with respect to accuracy, resilience, and efficiency.