research-article

Understanding the Detection of View Fraud in Video Content Portals

Authors:

Miriam Marciel,

Roberto González,

Stefano Traverso,

Arturo AzcorraAuthors Info & Claims

WWW '16: Proceedings of the 25th International Conference on World Wide Web

Pages 357 - 368

https://doi.org/10.1145/2872427.2882980

Published: 11 April 2016 Publication History

Abstract

While substantial effort has been devoted to understand fraudulent activity in traditional online advertising (search and banner), more recent forms such as video ads have received little attention. The understanding and identification of fraudulent activity (i.e., fake views) in video ads for advertisers, is complicated as they rely exclusively on the detection mechanisms deployed by video hosting portals. In this context, the development of independent tools able to monitor and audit the fidelity of these systems are missing today and needed by both industry and regulators.

In this paper we present a first set of tools to serve this purpose. Using our tools, we evaluate the performance of the audit systems of five major online video portals. Our results reveal that YouTube's detection system significantly outperforms all the others. Despite this, a systematic evaluation indicates that it may still be susceptible to simple attacks. Furthermore, we find that YouTube penalizes its videos' public and monetized view counters differently, the former being more aggressive. This means that views identified as fake and discounted from the public view counter are still monetized. We speculate that even though YouTube's policy puts in lots of effort to compensate users after an attack is discovered, this practice places the burden of the risk on the advertisers, who pay to get their ads displayed.

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Cited By

Castaldo MFrasca PVenturini TGargiulo F(2024)Fake views removal and popularity on YouTubeScientific Reports10.1038/s41598-024-63649-w14:1Online publication date: 4-Jul-2024
https://doi.org/10.1038/s41598-024-63649-w
González-Cabañas JCallejo PCuevas RSvartberg STorjesen TCuevas ÁPastor AKotila M(2023)CarbonTag: A Browser-Based Method for Approximating Energy Consumption of Online AdsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.32869168:4(739-750)Online publication date: Oct-2023
https://doi.org/10.1109/TSUSC.2023.3286916
Cao YXiao XSun RWang DXue MWen S(2023)StyleFool: Fooling Video Classification Systems via Style Transfer2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179383(1631-1648)Online publication date: May-2023
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Index Terms

Understanding the Detection of View Fraud in Video Content Portals
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Economics of security and privacy

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Published In

cover image ACM Other conferences

WWW '16: Proceedings of the 25th International Conference on World Wide Web

April 2016

1482 pages

ISBN:9781450341431

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Copyright © 2016 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 11 April 2016

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Spanish Ministry of Economy and Competitiveness
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WWW '16

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WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

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WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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González-Cabañas JCallejo PCuevas RSvartberg STorjesen TCuevas ÁPastor AKotila M(2023)CarbonTag: A Browser-Based Method for Approximating Energy Consumption of Online AdsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.32869168:4(739-750)Online publication date: Oct-2023
https://doi.org/10.1109/TSUSC.2023.3286916
Cao YXiao XSun RWang DXue MWen S(2023)StyleFool: Fooling Video Classification Systems via Style Transfer2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179383(1631-1648)Online publication date: May-2023
https://doi.org/10.1109/SP46215.2023.10179383
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