Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning

David Megías, Minoru Kuribayashi, Andrea Rosales, Krzysztof Cabaj, Wojciech Mazurczyk

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In today’s world, the ease of creation and distribution of fake news is becoming an increasing threat for individuals, companies, and institutions alike. Content spread over the Internet is able to create an “alternative” reality and false accusations cannot be easily removed by later issued apologies as it typically takes several years to unpick the labels pinned on by spreading disinformation. Currently, the main facilitators of fake news distribution are social media networks, where a large volume of digital media content is generated and exchanged every day. In this “flood” of information, it is quite effortless to manipulate the content to impact its consumers. That is why developing effective countermeasures is of prime importance. Considering the above, in this paper, we propose and describe an architecture of the fake news detection system that is being developed within an ongoing Detection of fake newS on SocIal MedIa pLAtfoRms (DISSIMILAR) project. It is designed for the protection of digital media content, i.e., images, video, and audio, and to fulfill its goals, it combines digital watermarking, signal processing, and machine learning techniques.

Original languageEnglish
Pages (from-to)33-55
Number of pages23
JournalJournal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
Issue number1
Publication statusPublished - Mar 2022


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

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science Applications
  • Computer Networks and Communications


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