Synthography: A term for synthetically created photo-realistic images

References

1. Anon. (1998), History of the Cornell Box, Ithaca, NY: Cornell University Program of Computer Graphics, https://www.graphics.cornell.edu/online/box/history.html. Accessed 12 May 2024.
   [Google Scholar]
2. Azoulay, Ariella (2010), ‘What is a photograph? What is photography?’, Philosophy of Photography, 1:1, pp. 9–13, https://bibliotheek.ehb.be:2102/10.1386/pop.1.1.9/7.
   [Google Scholar]
3. Barthes, Roland ([1980] 1982), La Chambre claire (Camera Lucida: Reflections on Photography), 2nd ed., New York City: Hill and Wang.
   [Google Scholar]
4. Bunz, Mercedes (2019), ‘The calculation of meaning: On the misunderstanding of new artificial intelligence as culture’, Culture, Theory and Critique, 60:3–4, pp. 264–78, https://bibliotheek.ehb.be:2102/10.1080/14735784.2019.1667255.
   [Google Scholar]
5. California State Legislature (2018), ‘Title 1.81.5.: California Consumer Privacy Act of 2018 [1798.100 - 1798.199.100]’, https://leginfo.legislature.ca.gov/faces/codes_displayText.xhtml?division=3.&part=4.&lawCode=CI V&title=1.81.5. Accessed 1 April 2024.
6. Carreira, João, Caseiro, Rui, Batista, Jorge and Sminchisescu, Cristian (2012), ‘Semantic segmentation with second-order pooling’, in 12th European Conference on Computer Vision (LNIP 7578), Florence, Italy, 7–13 October, pp. 430–43.
   [Google Scholar]
7. Chen, Liang-Chieh, Papandreou, George, Kokkinos, Iasonas, Murphy, Kevin and Yuille, Alan L. (2014), ‘Semantic image segmentation with deep convolutional nets and fully connected CRFs’, 3rd International Conference on Learning Representations, Proceedings, San Diego, CA, 7–9 May, https://bibliotheek.ehb.be:2102/10.48550/arXiv.1412.7062.
   [Google Scholar]
8. Deriche, Rachid (1987), ‘Using Canny’s criteria to derive a recursively implemented optimal edge detector’, International Journal of Computer Vision, 1:2, pp. 167–87.
   [Google Scholar]
9. Dzieza, Josh (2023), ‘AI is a lot of work: As the technology becomes ubiquitous, a vast tasker underclass is emerging – and it’s not going anywhere’, New York Magazine, 56:13, pp. 20–27, 89.
   [Google Scholar]
10. Elgammal, Ahmed, Liu, Bingchen, Elhoseiny, Mohamed and Mazzone, Marian (2017), ‘CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms’, Proceedings of the 8th International Conference on Computational Creativity (ICCC), Atlanta, GA, USA, 19–23 June, https://bibliotheek.ehb.be:2102/10.48550/arXiv.1706.07068.
    [Google Scholar]
11. European Parliament (2016), ‘Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)’, Brussels: European Parliament, http://data.europa.eu/eli/reg/2016/679/2016-05-04. Accessed 1 April 2024.
    [Google Scholar]
12. Gatys, Leon A., Ecker, Alexander S. and Bethge, Matthias (2016), ‘Image style transfer using convolutional neural networks’, in CVPR (ed.), Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June, Los Alamitos, CA, Washington, DC and Tokyo: IEEE Computer Society, Conference Publishing Services (CPS), pp. 2414–23.
    [Google Scholar]
13. Goodfellow, Ian J., Pouget-Abadie, Jean, Mirza, Mehdi, Xu Bing, Warde-Farle, David, Ozair, Sherjil, Courville, Aaron and Bengio, Yoshua (2014), ‘Generative adversarial networks’, Advances in Neural Information Processing Systems, 27, pp. 7354–63, https://bibliotheek.ehb.be:2102/10.48550/arXiv.1406.2661.
    [Google Scholar]
14. Huang, Yu-Hao and Fuh, Chiou-Shann (2009), Face Detection and Smile Detection, Taipei: National Taiwan University, Department of Computer Science and Information Engineering, https://csie.ntu.edu.tw/~fuh/personal/FaceDetectionandSmileDetection.pdf. Accessed 1 April 2024.
    [Google Scholar]
15. IKEA (eds) (2020), ‘After 70 successful years, IKEA is turning the page on the Catalog’, IKEA Newsroom, 13 December, https://web.archive.org/web/20201213071518/https://www.ikea.com/us/en/this-is-ikea/newsroom/after-70-successful-years-ikea-is-turning-the-page-on-the-catalog-pubad91dde0. Accessed 1 April 2023.
    [Google Scholar]
16. Johnson, Allison (2023), ‘Samsung’s moon photos are fake: But so is a lot of mobile photography’, The Verge, 14 March, https://theverge.com/2023/3/14/23640006/samsung-s23-moon-photo-controversy-space-zoom-computational-photography. Accessed 1 April 2024.
    [Google Scholar]
17. Kingma, Diederik P. and Welling, Max (2019), ‘An introduction to variational autoencoders’, Foundations and Trends^® in Machine Learning, 12:4, pp. 307–92, https://bibliotheek.ehb.be:2102/10.48550/arXiv.1906.02691.
    [Google Scholar]
18. Latour, Bruno (1993), We Have Never Been Modern, Cambridge, MA: Harvard University Press.
    [Google Scholar]
19. Liu, Bingchen, Zhu, Yizhe, Song, Kunpeng and Elgammal, Ahmed (2021), ‘Towards faster and stabilized GAN training for high-fidelity few-shot image synthesis’, in ICLR (ed.), Proceedings of the 9th International Conference on Learning Representations, Virtual Event, Austria, 3–7 May, Vienna: ICLR, https://bibliotheek.ehb.be:2102/10.48550/arXiv.2101.04775.
    [Google Scholar]
20. Mann, Steve (1996), ‘Method and apparatus for producing digital images having extended dynamic ranges’, United States Patent and Trademark Office Patent no. 5,828,793, 6 May, https://patents.google.com/patent/US5828793A/en. Accessed 1 April 2024.
    [Google Scholar]
21. Mildenhall, Ben, Srinivasan, Pratul P., Tancik, Matthew, Barron, Jonathan T., Ramamoorthi, Ravi and Ng, Ren (2021), ‘NeRF: Representing scenes as neural radiance fields for view synthesis’, Communications of the Association for Computing Machinery, 65:1, pp. 99–106, https://bibliotheek.ehb.be:2102/10.1145/3503250.
    [Google Scholar]
22. O’Neil, Cathy and Gunn, Hannah (2020), ‘Near-term artificial intelligence and the ethical matrix’, in S. M. Liao (ed.), Ethics of Artificial Intelligence, Oxford: Oxford University Press, pp. 237–70, https://bibliotheek.ehb.be:2102/10.1093/oso/9780190905040.003.0009.
    [Google Scholar]
23. Pachowsky, Milena L., Morf, Harriet, Simon, David, Schönau, Verena, Valor-Mendez, Larissa, Knitza, Johannes, Fagni, Filippo, Engel, Klaus, Uder, Michael, Hueber, Axel, Schmidkonz, Christian, Schett, Georg and Kleyer, Arnd (2022), ‘Cinematic rendering in rheumatic diseases: Photorealistic depiction of pathologies improves disease understanding for patients’, Frontiers in Medicine, 9, https://bibliotheek.ehb.be:2102/10.3389/fmed.2022.946106.
    [Google Scholar]
24. Parkin, Kirsty (2014), ‘Building 3D with IKEA’, CGSociety, 4 July, https://web.archive.org/web/20140704020336/http://www.cgsociety.org/index.php/CGSFeatures/CG SFeatureSpecial/building_3d_with_ikea. Accessed 1 April 2024.
    [Google Scholar]
25. Reinhuber, Elke (2022), ‘Synthography: An invitation to reconsider the rapidly changing toolkit of digital image creation as a new genre beyond photography’, in M. Wölfel, J. Bernhardt and S. Thiel (eds), ArtsIT, Interactivity and Game Creation: ArtsIT 2021 – Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 422, Cham: Springer, https://bibliotheek.ehb.be:2102/10.1007/978-3-030-95531-1_22.
    [Google Scholar]
26. Robertson, Barbara (2019), ‘Face-lift’, Computer Graphics World, 16 December, https://cgw.com/Publications/CGW/2019/Winter-2019/Face-Lift.aspx. Accessed 1 April 2024.
    [Google Scholar]
27. Saharia, Chitwan, Chan, William, Saxena, Saurabh, Li, Lala, Whang, Jay, Denton, Emily, Kamyar Seyed Ghasemipour, Seyed, Karagol Ayan, Burcu, Mahdavi, S. Sara, Gontijo Lopes, Rapha, Salimans, Tim, Ho, Jonathan, Fleet, David J. and Norouzi, Mohammad (2022), ‘Photorealistic text-to-image diffusion models with deep language understanding’, Advances in Neural Information Processing Systems, https://bibliotheek.ehb.be:2102/10.48550/arXiv.2205.11487.
    [Google Scholar]
28. Scharr, Hanno (2000), ‘Optimale Operatoren in der Digitalen Bildverarbeitung’, Inaugural dissertation, Jülich: Naturwissenschaftlich-Mathematische Gesamtfakultät der Ruprecht-Karls-Universität Heidelberg, https://researchgate.net/publication/36148383_Optimal_operators_in_digital_image_processing_Elektronische_Ressource. Accessed 12 April 2023.
    [Google Scholar]
29. Shutterstock (2023), ‘AI-generated content on Shutterstock: Contributor FAQ’, Shutterstock Contributor Support, https://support.submit.shutterstock.com/s/article/Shutterstock-ai-and-Computer-Vision-Contributor-FAQ?language=en_US. Accessed 1 April 2024.
    [Google Scholar]
30. Sloan, Robin J. S. (2012), ‘Why bridge the Uncanny Valley? Photorealism vs suspension of disbelief in animation’, Media Education Journal, 52, pp. 19–22.
    [Google Scholar]
31. Smith, Alvy R. (2021), A Biography of the Pixel, Cambridge, MA: MIT Press.
    [Google Scholar]
32. Tong, Xin, Wang, Luona, Pan, Xiaoqin and Wang, Jin gya (2020), ‘An overview of Deepfake: The sword of Damocles in AI’, in J. Guerrero (ed.), Proceedings of the 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), Chongqing, 10–12 July, Los Alamitos, CA, Piscataway, NJ, Tokyo: IEEE Computer Society, Conference Publishing Services, pp. 265–73, https://bibliotheek.ehb.be:2102/10.1109/CVIDL51233.2020.00-88.
    [Google Scholar]
33. Vogue Singapore (2023), ‘Welcome to the altiverse: AI avatars grace the cover of Vogue Singapore’s March issue’, Vogue Singapore, 3, p. 1, https://vogue.sg/vogue-singapores-march-issue-2023/. Accessed 1 April 2024.
    [Google Scholar]
34. Westerlund, Mika (2019), ‘The emergence of deepfake technology: A review’, Technology Innovation Management Review, 9:11, pp. 40–53.
    [Google Scholar]
35. Yiu, Sheung (2023), ‘One face, millions of faces: Computer vision as hyperobject’, Philosophy of Photography, 12:1, pp. 71–91, https://bibliotheek.ehb.be:2102/10.1386/pop_00048_1.
    [Google Scholar]
36. Zhang, Han, Goodfellow, Ian, Metaxas, Dimitris and Odena, Augustus (2019), ‘Self-attention generative adversarial networks’, Proceedings of the 36th International Conference on Machine Learning, vol. 97, Long Beach, CA, USA, 9–15 June, Red Hook, NY: Curran Associates, Inc., pp. 7354–63.
    [Google Scholar]