Currently, the rapid development of computer vision and deep learning has enabled the creation or manipulation of high-fidelity facial images and videos via deep generative approaches. This technology, also known as deepfake, has achieved dramatic progress and become increasingly popular in social media. However, the technology can generate threats to personal privacy and national security by spreading misinformation. To diminish the risks of deepfake, it is desirable to develop powerful forgery detection methods to distinguish fake faces from real faces. This paper presents a comprehensive survey of recent deep learning-based approaches for facial forgery detection. We attempt to provide the reader with a deeper understanding of the current advances as well as the major challenges for deepfake detection based on deep learning. We present an overview of deepfake techniques and analyse the characteristics of various deepfake datasets. We then provide a systematic review of different categories of deepfake detection and state-of-the-art deepfake detection methods. The drawbacks of existing detection methods are analyzed, and future research directions are discussed to address the challenges in improving both the performance and generalization of deepfake detection.

翻译：当前，计算机视觉与深度学习的快速发展使得通过深度生成方法创建或操纵高保真人脸图像与视频成为可能。这项技术亦被称为深度伪造，已在社交媒体领域取得显著进展并日益普及。然而，该技术可能通过传播虚假信息对个人隐私及国家安全构成威胁。为降低深度伪造风险，亟需开发强大的伪造检测方法以区分伪造人脸与真实人脸。本文对近期基于深度学习的人脸伪造检测方法进行全面综述。我们试图使读者更深入地理解当前基于深度学习的深度伪造检测进展及主要挑战。本文首先概述深度伪造技术，分析各类深度伪造数据集的特征；随后系统回顾不同类别的深度伪造检测方法及最先进的检测技术；最后剖析现有检测方法的局限性，并探讨未来研究方向以应对提升检测性能与泛化能力所面临的挑战。