Approaches for Fake Content Detection: Strengths and Weaknesses to Adversarial Attacks
| Autor: | Sherali Zeadally, Michail Tsikerdekis, Matthew Carter |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Network security business.industry Computer science Deep learning Feature extraction 020206 networking & telecommunications 02 engineering and technology Computer security computer.software_genre Adversarial system 0202 electrical engineering electronic engineering information engineering The Internet Artificial intelligence Fake news business Content (Freudian dream analysis) computer Strengths and weaknesses |
| Zdroj: | IEEE Internet Computing. 25:73-83 |
| ISSN: | 1941-0131 1089-7801 |
| DOI: | 10.1109/mic.2020.3032323 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|