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

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 apologi...

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Detalles Bibliográficos
Autores: Megias, David, Kuribayashi, Minoru, Rosales, Andrea, Cabaj, Krzysztof, Mazurczyk, Wojciech
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2022
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/149832
Acceso en línea:http://hdl.handle.net/10609/149832
https://doi.org/10.22667/JOWUA.2022.03.31.033
Access Level:acceso abierto
Palabra clave:fake news
digital watermarking
machine learning
signal processing
user experience study
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spelling Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learningMegias, DavidKuribayashi, MinoruRosales, AndreaCabaj, KrzysztofMazurczyk, Wojciechfake newsdigital watermarkingmachine learningsignal processinguser experience studyIn 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 coun- termeasures 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.Innovative Information Science & Technology Research Group202420242022info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10609/149832https://doi.org/10.22667/JOWUA.2022.03.31.033reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésJournal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 2022, 13(1):33-55,https://doi.org/10.22667/JOWUA.2022.03.31.033© The Authors 2022info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1498322026-05-28T12:42:01Z
dc.title.none.fl_str_mv Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning
title Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning
spellingShingle Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning
Megias, David
fake news
digital watermarking
machine learning
signal processing
user experience study
title_short Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning
title_full Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning
title_fullStr Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning
title_full_unstemmed Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning
title_sort Architecture of a fake news detection system combining digital watermarking, signal processing, and machine learning
dc.creator.none.fl_str_mv Megias, David
Kuribayashi, Minoru
Rosales, Andrea
Cabaj, Krzysztof
Mazurczyk, Wojciech
author Megias, David
author_facet Megias, David
Kuribayashi, Minoru
Rosales, Andrea
Cabaj, Krzysztof
Mazurczyk, Wojciech
author_role author
author2 Kuribayashi, Minoru
Rosales, Andrea
Cabaj, Krzysztof
Mazurczyk, Wojciech
author2_role author
author
author
author
dc.subject.none.fl_str_mv fake news
digital watermarking
machine learning
signal processing
user experience study
topic fake news
digital watermarking
machine learning
signal processing
user experience study
description 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 coun- termeasures 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10609/149832
https://doi.org/10.22667/JOWUA.2022.03.31.033
url http://hdl.handle.net/10609/149832
https://doi.org/10.22667/JOWUA.2022.03.31.033
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 2022, 13(1):33-55,
https://doi.org/10.22667/JOWUA.2022.03.31.033
dc.rights.none.fl_str_mv © The Authors 2022
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © The Authors 2022
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Innovative Information Science & Technology Research Group
publisher.none.fl_str_mv Innovative Information Science & Technology Research Group
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,228081

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