Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is deepfake. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable. This paper presents a survey of algorithms used to create deepfakes and, more importantly, methods proposed to detect deepfakes in the literature to date. We present extensive discussions on challenges, research trends and directions related to deepfake technologies. By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.

翻译：深层学习已被成功应用,以解决从大数据分析到计算机视觉和人力控制等各种复杂问题。深层学习进步也被用于创建软件,对隐私、民主和国家安全构成威胁。最近出现的深层学习动力应用之一是深假。深假算法可以制作假图像和视频,人类无法将其与真实的图像和视频区分开来。因此,提出能够自动检测和评估数字视觉媒体完整性的技术是不可或缺的。本文介绍了用于创造深层假象的算法调查,更重要的是,为发现迄今在文献中的深层假象而提出的方法。我们广泛讨论了与深层假象技术有关的挑战、研究趋势和方向。通过审查深假和最先进的深假探测方法的背景,本研究报告全面概述了深假技术,并促进开发新的、更强有力的方法,以应对日益具有挑战性的深层假象。