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 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，因为这些模型能够生成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，迫切需要开发新的计算模型，以有效检测伪造内容并提醒用户注意潜在的图像和视频篡改。本文对近年来使用基于深度学习的方法进行深度伪造内容检测的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，拓宽当前最新研究范围。此外，我们报告了所审查工作的优缺点，以及针对深度伪造检测中尚未解决的关键问题与不足的未来研究方向。