CNSC Andrea Rosales professor co-authored “DISSIMILAR: Towards fake news detection using information hiding, signal processing and machine learning” on ARES 2021: The 16th International Conference on Availability, Reliability and Security of the Association for Computing Machinery.
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.
How to cite
David Megías, Minoru Kuribayashi, Andrea Rosales, and Wojciech Mazurczyk. 2021. DISSIMILAR: Towards fake news detection using information hiding, signal processing and machine learning. In The 16th International Conference on Availability, Reliability and Security (ARES 2021). Association for Computing Machinery, New York, NY, USA, Article 66, 1–9. https://doi.org/10.1145/3465481.3470088
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