  Recent advancements in deep learning generative models have raised concerns
as they can create highly convincing counterfeit images and videos. This poses
a threat to people's integrity and can lead to social instability. To address
this issue, there is a pressing need to develop new computational models that
can efficiently detect forged content and alert users to potential image and
video manipulations. This paper presents a comprehensive review of recent
studies for deepfake content detection using deep learning-based approaches. We
aim to broaden the state-of-the-art research by systematically reviewing the
different categories of fake content detection. Furthermore, we report the
advantages and drawbacks of the examined works, and prescribe several future
directions towards the issues and shortcomings still unsolved on deepfake
detection.
