Publication

Forged Authenticity: Governing Deepfake Risks

2019
Reports

Abstract

The manipulation of digital content is not a new phenomenon, but the application of machine learning to the creation of deepfakes has (i) radically improved the quality of output, (ii) slashed the resources required to produce realistic fakes at previously unimaginable scale, and (iii) “democratized” the process with user-friendly tools and services. The proliferation of increasingly realistic fabricated content presents numerous potential risks to individuals, organizations and societies. This report builds on the proceedings of an interdisciplinary expert workshop on deepfakes held by the EPFL International Risk Governance Center in September 2019. It suggests a simple framework for categorizing and prioritizing potential deepfake risks, before providing an overview of 15 potential technological, legal and societal responses.

Official source

https://infoscience.epfl.ch/entities/publication/cadd8310-7839-407e-abae-8e8fc920cc43

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2019
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/cadd8310-7839-407e-abae-8e8fc920cc43

About this result

Related concepts (15)

Deepfake

Deepfakes (portmanteau of "deep learning" and "fake") are synthetic media that have been digitally manipulated to replace one person's likeness convincingly with that of another. Deepfakes are the manipulation of facial appearance through deep generative methods. While the act of creating fake content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence to manipulate or generate visual and audio content that can more easily deceive.

Synthetic media

Synthetic media (also known as AI-generated media, media produced by generative AI, personalized media, and colloquially as deepfakes) is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means, especially through the use of artificial intelligence algorithms, such as for the purpose of misleading people or changing an original meaning.

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

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