Innovation or Piracy? Empirically Demarcating AI Painting Copyright Infringement Boundary
- DOI
- 10.2991/978-94-6463-200-2_143How to use a DOI?
- Keywords
- AI Painting; Substantial Similarity; Idea-expression Dichotomy; Copyright Infringement; Overfitting; Stable Diffusion
- Abstract
In the face of intense debates regarding AI painting copyright infringement, this study argues that AI painting should be considered a distinct art form that should be regulated separately from collage works because only the small-memory trained algorithm advanced to the algorithm creative stage, while the possibly copyright-infringing collage dataset did not. The sporadic occurrence of substantial similarity is attributable to the aberrant overfitting of AI painting algorithms, which will replicate the expression of the original work. If AI algorithm providers are aware of overfitting and do not attempt to avoid it or include additional filtering algorithms, it should be considered piracy. In contrast, other appropriately fitting AI paintings do not constitute copyright infringement since it is a process of integrating ideas rather than collaging expression according to the idea-expression dichotomy.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Zihang Lan AU - Shuhan Yang AU - Rui Fan AU - Bo Zhao AU - Yanru Yan PY - 2023 DA - 2023/07/26 TI - Innovation or Piracy? Empirically Demarcating AI Painting Copyright Infringement Boundary BT - Proceedings of the 2023 3rd International Conference on Public Management and Intelligent Society (PMIS 2023) PB - Atlantis Press SP - 1328 EP - 1341 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-200-2_143 DO - 10.2991/978-94-6463-200-2_143 ID - Lan2023 ER -