DISSIMILAR: Towards fake news detection using information hiding, signal processing and machine learning

David Megías, Minoru Kuribayashi, Andrea Rosales, Wojciech Mazurczyk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Digital media have changed the classical model of mass media that considers the transmitter of a message and a passive receiver, to a model where users of the digital media can appropriate the contents, recreate, and circulate them. In this context, online social media are a suitable circuit for the distribution of fake news and the spread of disinformation. Particularly, photo and video editing tools and recent advances in artificial intelligence allow non-professionals to easily counterfeit multimedia documents and create deep fakes. To avoid the spread of disinformation, some online social media deploy methods to filter fake content. Although this can be an effective method, its centralized approach gives an enormous power to the manager of these services. Considering the above, this paper outlines the main principles and research approach of the ongoing DISSIMILAR project, which is focused on the detection of fake news on social media platforms using information hiding techniques, in particular, digital watermarking, combined with machine learning approaches.

Original languageEnglish
Title of host publication16th International Conference on Availability, Reliability and Security, ARES 2021
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450390514
DOIs
Publication statusPublished - Aug 17 2021
Event16th International Conference on Availability, Reliability and Security, ARES 2021 - Virtual, Online, Austria
Duration: Aug 17 2021Aug 20 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference16th International Conference on Availability, Reliability and Security, ARES 2021
Country/TerritoryAustria
CityVirtual, Online
Period8/17/218/20/21

Keywords

  • digital watermarking
  • Fake news
  • machine learning
  • signal processing
  • user experience study

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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