Deep learning and synthetic media

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media.
Original languageEnglish
Article number231
Pages (from-to)1-27
Number of pages27
JournalSynthese
Volume200
Issue number3
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

Keywords

  • AI
  • Art
  • Deep learning
  • Deepfakes
  • Depiction
  • Disinformation
  • Media synthesis

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