With the rapid progress in recent years, techniques that generate and manipulate multimedia content can now provide a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, and video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos. These can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Therefore, there is an urgent need for automated tools capable of detecting false multimedia content and avoiding the spread of dangerous false information. This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos. Special emphasis will be placed on the emerging phenomenon of deepfakes, fake media created through deep learning tools, and on modern data-driven forensic methods to fight them. The analysis will help highlight the limits of current forensic tools, the most relevant issues, the upcoming challenges, and suggest future directions for research.

中文翻译：

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媒体取证和 DeepFakes：概述


随着近年来的快速发展，生成和操作多媒体内容的技术现在可以提供非常先进的真实感水平。真实媒体和合成媒体之间的界限已经变得非常薄弱。一方面，这为创意艺术、广告、电影制作和视频游戏等不同领域的一系列令人兴奋的应用打开了大门。另一方面，它也带来了巨大的安全威胁。网络上免费提供的软件包允许任何个人，无需特殊技能，即可创建非常逼真的虚假图像和视频。这些可用于在选举期间操纵公众舆论、实施欺诈、抹黑或勒索他人。因此，迫切需要能够检测虚假多媒体内容并避免危险虚假信息传播的自动化工具。本文旨在分析视觉媒体完整性验证的方法，即操纵图像和视频的检测。将特别强调新兴的深度造假现象、通过深度学习工具创建的虚假媒体，以及现代数据驱动的取证方法来对抗这些现象。该分析将有助于突出当前取证工具的局限性、最相关的问题、即将到来的挑战，并提出未来的研究方向。