A Review of Deep Learning-based Approaches for Deepfake Content Detection
This work provides a comprehensive overview for researchers and practitioners in cybersecurity and media forensics, but it is incremental as it synthesizes existing studies without introducing new methods.
The paper reviews deep learning-based methods for detecting deepfake content, addressing the problem of identifying forged images and videos created by advanced generative models, and it systematically categorizes existing approaches while highlighting their strengths and weaknesses.
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.