Olga Papadopoulou
Symeon Papadopoulos
Yiannis Kompatsiaris
ABSTRACT
As news agencies and the public increasingly rely on User-Generated Content, content verification is vital for news producers and consumers alike. We present a novel approach for verifying Web videos by analyzing their online context. It is based on supervised learning on contextual features: one feature set is based on an existing approach for tweet verification adapted to video comments. The other is based on video metadata, such as the video description, likes/dislikes, and uploader information. We evaluate both on a dataset of real and fake videos from YouTube, and demonstrate their effectiveness (F-scores: 0.82, 0.79). We then explore their complementarity and show that under an optimal fusion scheme, the classifier would reach an F-score of 0.9. We finally study the performance of the classifier through time, as more comments accumulate, emulating a real-time verification setting.
- Christina Boididou, Katerina Andreadou, Symeon Papadopoulos, Duc-Tien Dang-Nguyen, Giulia Boato, Michael Riegler, and Yiannis Kompatsiaris. 2015. Verifying Multimedia Use at MediaEval 2015. In MediaEval 2015 Workshop, Sept. 14-15, 2015, Wurzen, Germany.Google Scholar
- Christina Boididou, Stuart E. Middleton, Symeon Papadopoulos, Dang Nguyen, Duc Tien, Michael Riegler, Giulia Boato, Andreas Petlund, and Yiannis Kom- patsiaris. 2016. The VMU Participation @ verifying multimedia use 2016. In MediaEval Benchmarking Initiative for Multimedia Evaluation 2016. CEUR-WS.Google Scholar
- Christina Boididou, Symeon Papadopoulos, Duc-Tien Dang-Nguyen, Giulia Boato, Michael Riegler, Stuart E. Middleton, Andreas Petlund, and Yiannis Kompatsiaris. 2016. Verifying Multimedia Use at MediaEval 2016. In MediaEval, Vol. 1739. CEUR-WS.org.Google Scholar
- Aditi Gupta, Hemank Lamba, Ponnurangam Kumaraguru, and Anupam Joshi. 2013. Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy. In 22nd International World Wide Web Conference, WWW '13. ACM, 729--736. Google ScholarDigital Library
- Ramesh Chand Pandey, Sanjay Kumar Singh, and Kaushal K. Shukla. 2016. Pas- sive forensics in image and video using noise features: A review. Digital Inves- tigation 19 (2016), 1--28. Google ScholarDigital Library
- Symeon Papadopoulos, Markos Zampoglou, Ioannis Kompatsiaris, and Denis Teyssou. 2017. InVID Fake Video Corpus. (Jan. 2017).Google Scholar
- Meet Rajdev and Kyumin Le. 2015. Fake and Spam Messages: Detecting Mis-information During Natural Disasters on Social Media. In WI-IAT (1). IEEE Computer Society, 17--20. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7395995 Google ScholarDigital Library
- Ewerton Silva, Tiago Jose de Carvalho, Anselmo Ferreira, and Anderson Rocha. 2015. Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes. J. Visual Communication and Im- age Representation 29 (2015), 16--32. Google ScholarDigital Library
- Neil Thurman, Steve Schifferes, Richard Fletcher, Nic Newman, Stephen Hunt, and Aljosha Karim Schapals. 2016. Giving Computers a Nose for News. Digital Journalism 4, 7 (2016), 838--848.Google ScholarCross Ref
- Markos Zampoglou, Symeon Papadopoulos, and Yiannis Kompatsiaris. 2017. A Large-Scale Evaluation of Splicing Localization Algorithms for Web Images. Multimedia Tools and Applications 76, 4 (February 2017), 4801--4834. Google ScholarDigital Library
- Markos Zampoglou, Symeon Papadopoulos, Yiannis Kompatsiaris, Ruben Bouwmeester, and Jochen Spangenberg. 2016. Web and Social Media Image Forensics for News Professionals. In SMN@ICWSM (AAAI Workshops), Jisun An, Haewoon Kwak, and Fabrício Benevenuto (Eds.), Vol. WS-16-19. AAAI Press. http://www.aaai.org/Library/Workshops/ws16-19.phpGoogle Scholar
Index Terms
- Web Video Verification using Contextual Cues
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