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Analysis and Detection of Fake Views in Online Video Services

Analysis and Detection of Fake Views in Online Video Services Analysis and Detection of Fake Views in Online Video Services LIANG CHEN and YIPENG ZHOU, Shenzhen University DAH MING CHIU, The Chinese University of Hong Kong Online video-on-demand(VoD) services invariably maintain a view count for each video they serve, and it has become an important currency for various stakeholders, from viewers, to content owners, advertizers, and the online service providers themselves. There is often significant financial incentive to use a robot (or a botnet) to artificially create fake views. How can we detect fake views? Can we detect them (and stop them) efficiently? What is the extent of fake views with current VoD service providers? These are the questions we study in this article. We develop some algorithms and show that they are quite effective for this problem. Categories and Subject Descriptors: C.2.3 [Computer-Communication Networks]: Network Operations-- Network management, network monitoring; I.5.2 [Pattern Recognition]: Design Methodology--Classifier design and evaluation, feature evaluation and selection, pattern analysis General Terms: Measurement, Experimentation, Reliability Additional Key Words and Phrases: Fake view, online video service ACM Reference Format: Liang Chen, Yipeng Zhou, and Dah Ming Chiu. 2015. Analysis and detection of fake views in online video services. ACM Trans. Multimedia Comput. Commun. Appl. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) Association for Computing Machinery

Analysis and Detection of Fake Views in Online Video Services

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
1551-6857
DOI
10.1145/2700290
Publisher site
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Abstract

Analysis and Detection of Fake Views in Online Video Services LIANG CHEN and YIPENG ZHOU, Shenzhen University DAH MING CHIU, The Chinese University of Hong Kong Online video-on-demand(VoD) services invariably maintain a view count for each video they serve, and it has become an important currency for various stakeholders, from viewers, to content owners, advertizers, and the online service providers themselves. There is often significant financial incentive to use a robot (or a botnet) to artificially create fake views. How can we detect fake views? Can we detect them (and stop them) efficiently? What is the extent of fake views with current VoD service providers? These are the questions we study in this article. We develop some algorithms and show that they are quite effective for this problem. Categories and Subject Descriptors: C.2.3 [Computer-Communication Networks]: Network Operations-- Network management, network monitoring; I.5.2 [Pattern Recognition]: Design Methodology--Classifier design and evaluation, feature evaluation and selection, pattern analysis General Terms: Measurement, Experimentation, Reliability Additional Key Words and Phrases: Fake view, online video service ACM Reference Format: Liang Chen, Yipeng Zhou, and Dah Ming Chiu. 2015. Analysis and detection of fake views in online video services. ACM Trans. Multimedia Comput. Commun. Appl.

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) – Association for Computing Machinery

Published: Feb 24, 2015

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