Approaches for Fake Content Detection: Strengths and Weaknesses to Adversarial Attacks

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.

Automated summary

In recent years, there has been a rapid increase in fake content on the Internet, threatening the veracity of information. Advanced technologies like machine learning are being used to detect this fake content, but they still have weaknesses. The article describes methods for detecting fake content and potential adversarial attacks, while also discussing future research challenges in this area.