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AI-based Image Generation Algorithm and Photography

  • The Journal of Aesthetics and Science of Art
  • Abbr : JASA
  • 2021, 62(), pp.198-222
  • DOI : 10.17527/JASA.62.0.08
  • Publisher : 한국미학예술학회
  • Research Area : Arts and Kinesiology > Other Arts and Kinesiology
  • Received : December 10, 2020
  • Accepted : January 9, 2021
  • Published : February 28, 2021

Pyung-jong Park 1

1중앙대학교 인문콘텐츠연구소

Accredited

ABSTRACT

This article deals with how images produced using artificial intelligence-based algorithms differ from existing photography. A generative adversarial network (GAN) is an algorithm that calculates fake data through balanced learning of generators and discriminators. When using original photos as learning data, it creates images that are visually indistinguishable from photos. There are two issues raised by this. First, the algorithmic image cannot be called a photograph from the point of view of index theory, but it is no different from the existing digital photograph in that it creates a new image by changing the pixel value of the original photograph. Second, the algorithmic image is an advanced form of program automatism and human exclusion, which Vilem Flusser defined as the core of the technical image. Humans are excluded from the production process of the GAN algorithm image. In that sense, the generator of the GAN is a black box. As the automaticity of the program increases, humans do not control image production and become simple consumers. Therefore, it is time for humans to think about how to turn a black box called a program into a “transparent box.”

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.