Paper Info
Title
Caught Red-Handed: Toward Practical Video-Based Subsequences Matching in the Presence of Real-World Transformations
Abstract
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.
Year
DOI
Venue
2017
10.1109/CVPRW.2017.182
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Keywords
Field
DocType
Videos,Robustness,YouTube,Feature extraction,Advertising,NIST
Psychological resilience,Revenue,Computer vision,Social network,Incentive,Computer science,Upload,Robustness (computer science),Feature extraction,Artificial intelligence,Multimedia
Conference
Volume
Issue
ISSN
2017
1
2160-7508
ISBN
Citations 
PageRank 
978-1-5386-0733-6
1
0.34
References 
Authors
27
4
Name
Order
Citations
PageRank
Yi Xu11757177.61
True Price2555.07
Fabian Monrose33448257.07
Jan-Michael Frahm42847141.20