Due to the widespread use of smartphones with high-quality digital cameras and easy access to a wide range of software apps for recording, editing, and sharing videos and images, as well as the deep learning AI platforms, a new phenomenon of 'faking' videos has emerged. Deepfake algorithms can create fake images and videos that are virtually indistinguishable from authentic ones. Therefore, technologies that can detect and assess the integrity of digital visual media are crucial. Deepfakes, also known as deep learning-based fake videos, have become a major concern in recent years due to their ability to manipulate and alter images and videos in a way that is virtually indistinguishable from the original. These deepfake videos can be used for malicious purposes such as spreading misinformation, impersonating individuals, and creating fake news. Deepfake detection technologies use various approaches such as facial recognition, motion analysis, and audio-visual synchronization to identify and flag fake videos. However, the rapid advancement of deepfake technologies has made it increasingly difficult to detect these videos with high accuracy. In this paper, we aim to provide a comprehensive review of the current state of deepfake creation and detection technologies. We examine the various deep learning-based approaches used for creating deepfakes, as well as the techniques used for detecting them. Additionally, we analyze the limitations and challenges of current deepfake detection methods and discuss future research directions in this field. Overall, the paper highlights the importance of continued research and development in deepfake detection technologies in order to combat the negative impact of deepfakes on society and ensure the integrity of digital visual media.

翻译：由于搭载高质量数字摄像头的智能手机广泛普及，加之用于录制、编辑和分享视频与图像的各类软件应用以及深度学习AI平台易于获取，一种新型的"伪造"视频现象应运而生。深度伪造算法能够创建与真实内容几乎无法区分的虚假图像和视频。因此，能够检测并评估数字视觉媒体完整性的技术至关重要。深度伪造（又称基于深度学习的伪造视频）因其能以几乎无法与原始内容区分的方式操纵和篡改图像与视频的能力，近年来已成为重大关注点。这些深度伪造视频可能被用于恶意目的，如传播错误信息、冒充他人身份以及制造假新闻。深度伪造检测技术采用面部识别、运动分析和视听同步等多种方法来识别和标记虚假视频。然而，深度伪造技术的快速发展使得高精度检测这些视频变得越来越困难。本文旨在对深度伪造创建与检测技术的现状进行全面综述。我们研究了用于创建深度伪造的各种深度学习方法，以及检测这些方法所采用的技术。此外，我们还分析了当前深度伪造检测方法的局限与挑战，并讨论了该领域的未来研究方向。总体而言，本文强调了持续推进深度伪造检测技术研发以消除深度伪造对社会的负面影响、确保数字视觉媒体完整性的重要意义。