TY - GEN
T1 - DISSIMILAR
T2 - 16th International Conference on Availability, Reliability and Security, ARES 2021
AU - Megías, David
AU - Kuribayashi, Minoru
AU - Rosales, Andrea
AU - Mazurczyk, Wojciech
N1 - Funding Information:
The authors acknowledge the funding obtained from the EIG CONCERT-Japan call to the project Detection of fake newS on SocIal MedIa pLAtfoRms “DISSIMILAR” through grants PCI2020-120689-2 (Spanish Government), JPMJSC20C3 (Japanese Government) and a grant funded by the National Centre for Research and Development (Poland). The first author also acknowledges the funding to the project RTI2018-095094-B-C22 “CONSENT” by the Spanish Ministry of Science and Innovation.
Publisher Copyright:
© 2021 ACM.
PY - 2021/8/17
Y1 - 2021/8/17
N2 - 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.
AB - 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.
KW - digital watermarking
KW - Fake news
KW - machine learning
KW - signal processing
KW - user experience study
UR - http://www.scopus.com/inward/record.url?scp=85113199723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85113199723&partnerID=8YFLogxK
U2 - 10.1145/3465481.3470088
DO - 10.1145/3465481.3470088
M3 - Conference contribution
AN - SCOPUS:85113199723
T3 - ACM International Conference Proceeding Series
BT - 16th International Conference on Availability, Reliability and Security, ARES 2021
PB - Association for Computing Machinery
Y2 - 17 August 2021 through 20 August 2021
ER -