In the last few years, we have witnessed an explosive growth of fake content on the Internet which has significantly affected the veracity of information on many social platforms. Much of this disruption has been caused by the proliferation of advanced machine and deep learning methods. In turn, social platforms have been using the same technological methods in order to detect fake content. However, there is understanding of the strengths and weaknesses of these detection methods. In this article, we describe examples of machine and deep learning approaches that can be used to detect different types of fake content. We also discuss the characteristics and the potential for adversarial attacks on these methods that could reduce the accuracy of fake content detection. Finally, we identify and discuss some future research challenges in this area.
|Number of pages||11|
|Journal||IEEE Internet Computing|
|State||Published - Mar 1 2021|
Bibliographical notePublisher Copyright:
© 1997-2012 IEEE.
ASJC Scopus subject areas
- Computer Networks and Communications