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.

翻译：近年来，深度学习生成模型的进展引发了担忧，因为它们能够创建高度逼真的伪造图像和视频。这对人们的诚信构成威胁，并可能导致社会不稳定。为解决这一问题，迫切需要开发能够高效检测伪造内容并警示用户潜在图像和视频篡改的新型计算模型。本文对近期采用深度学习方法的深度伪造内容检测研究进行了全面综述。我们旨在通过系统梳理不同类别的伪造内容检测研究，拓展该领域的最新技术现状。此外，我们报告了所考察工作的优缺点，并针对深度伪造检测中尚未解决的问题和不足，指出了若干未来研究方向。