Editing Style (visual arts) (section)

==Computer identification and recreation==
In a 2012 experiment at [[Lawrence Technological University]] in Michigan, a computer analysed approximately 1,000 [[painting]]s from 34 well-known artists using a specially developed algorithm and placed them in similar style categories to human art historians.<ref name=tracy_scicomp>{{Citation |editor=Suzanne Tracy |title=Computers Match Humans in Understanding Art |work=Scientific Computing |url=http://www.scientificcomputing.com/news-DA-Computers-Match-Humans-in-Understanding-Art-100112.aspx |access-date=November 2, 2012 }} A summary of: {{Citation |author=Lior Shamir, Jane A. Tarakhovsky |title=Computer analysis of art |work=Journal on Computing and Cultural Heritage (JOCCH) 5.2 (2012) |url=https://dl.acm.org/doi/10.1145/2307723.2307726 }}</ref>  The analysis involved the sampling of more than 4,000 visual features per work of art.<ref name=tracy_scicomp/><ref>See also Gombrich, 140, commenting in 1968 that no such analysis was feasible at that time.</ref>

Apps such as Deep Art Effects can turn photos into art-like images claimed to be in the style of painters such as [[Van Gogh]].<ref>{{cite news |title=A.I. photo filters use neural networks to make photos look like Picassos |url=https://www.digitaltrends.com/mobile/best-ai-based-photo-apps/ |access-date=9 November 2022 |work=Digital Trends |date=18 November 2019 |language=en}}</ref><ref>{{cite news |last1=Biersdorfer |first1=J. D. |title=From Camera Roll to Canvas: Make Art From Your Photos |url=https://www.nytimes.com/2019/12/04/technology/personaltech/turn-photos-into-paintings.html |access-date=9 November 2022 |work=The New York Times |date=4 December 2019}}</ref> With the development of sophisticated [[Artificial intelligence art#Text-to-image models|text-to-image AI art software]], using specifiable art styles has become a widespread tool in the 2020s.<ref>{{Cite arXiv|last1=Gal |first1=Rinon |last2=Alaluf |first2=Yuval |last3=Atzmon |first3=Yuval |last4=Patashnik |first4=Or |last5=Bermano |first5=Amit H. |last6=Chechik |first6=Gal |last7=Cohen-Or |first7=Daniel |title=An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion |date=2 August 2022|class=cs.CV |eprint=2208.01618 }}</ref><ref>{{cite news |last1=Vincent |first1=James |title=DALL-E can now help you imagine what's outside the frame of famous paintings |url=https://www.theverge.com/2022/9/5/23337580/openai-dall-e-text-to-image-generator-outpainting-native-function |access-date=9 November 2022 |work=The Verge |date=5 September 2022}}</ref><ref>{{cite news |last1=Edwards |first1=Benj |title=With Stable Diffusion, you may never believe what you see online again |url=https://arstechnica.com/information-technology/2022/09/with-stable-diffusion-you-may-never-believe-what-you-see-online-again/ |access-date=9 November 2022 |work=Ars Technica |date=6 September 2022 |language=en-us}}</ref><ref>{{cite news |last1=James |first1=Dave |title=I thrashed the RTX 4090 for 8 hours straight training Stable Diffusion to paint like my uncle Hermann |url=https://www.pcgamer.com/nvidia-rtx-4090-stable-diffusion-training-aharon-kahana/ |access-date=9 November 2022 |work=PC Gamer |date=27 October 2022 |language=en}}</ref><ref>{{cite magazine |last1=Ford |first1=Paul |title=Dear Artists: Do Not Fear AI Image Generators |url=https://www.wired.com/story/artists-do-not-fear-ai-image-generators/ |date=Nov 3, 2022  |access-date=9 November 2022 |magazine=Wired}}</ref><ref>{{cite news |last1=Metz |first1=Rachel |title=These artists found out their work was used to train AI. Now they're furious  |url=https://edition.cnn.com/2022/10/21/tech/artists-ai-images/index.html |access-date=9 November 2022 |work=CNN Business |date=21 October 2022 |language=en}}</ref>