Media Forensics and DeepFakes: An Overview

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 diffe...

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Veröffentlicht in:IEEE journal of selected topics in signal processing Jg. 14; H. 5; S. 910 - 932
1. Verfasser: Verdoliva, Luisa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Computer & video games
Deception
Deep learning
deepfakes
Digital forensics
digital image forensics
Elections
Forensics
Fraud
Generative adversarial networks
Image manipulation
Information integrity
Machine learning
Media
Multimedia
video forensics
Videos
ISSN:1932-4553, 1941-0484
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2020.3002101