THE EVOLUTION OF POST-PHOTOGRAPHY ART IN AN ERA DOMINATED BY AI IMAGE SYNTHESIS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7476Keywords:
Post-Photography, Artificial Intelligence Art, Image Synthesis, Generative Models, Algorithmic Creativity, Computational AestheticsAbstract [English]
The advent of image generation via artificial intelligence has drastically changed the basis of the visual culture and modern day art practice. Historically, photography has served as a tool of recording reality, where optical capture and physical cameras are used to capture visual data provided by the outside world. Nevertheless, more recent developments in machine learning, especially generative models like Generative Adversarial Networks (GANs), diffusion models or neural rendering systems have made possible the production of exceptionally realistic images by pairing this technique with no physical photographic capture. In this technology change, post-photography is a conceptual and artistic paradigm where images are produced, altered or synthesized by a computation instead of being captured using conventional photographic processes. Artists in this new paradigm are starting to work more and more closely with AI systems, whereby algorithmic tools are used to discover new aesthetics, generative workflows and creative human-machine hybridity. Democratization of image creation has also been enabled by the availability of AI based visual tools and people with minimal technical expertise can create advanced visual arts
References
Başarir, L. (2022). Modelling AI in Architectural Education. Gazi University Journal of Science, 35, 1260–1278. https://doi.org/10.35378/gujs.967981
Bird, J. J., and Lotfi, A. (2023). CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images. arXiv preprint arXiv:2303.14126. https://doi.org/10.1109/ACCESS.2024.3356122
Du, Z., Zeng, A., Dong, Y., and Tang, J. (2024). Understanding Emergent Abilities of Language Models from the Loss Perspective. arXiv preprint arXiv:2403.15796.
Grover, R., Emmitt, S., and Copping, A. (2020). Critical Learning for Sustainable Architecture: Opportunities for Design Studio Pedagogy. Sustainable Cities and Society, 53, 101876. https://doi.org/10.1016/j.scs.2019.101876
Lambright, K. (2023). The Effect of a Teacher's Mindset on the Cascading Zones of Proximal Development: A Systematic Review. Technology, Knowledge and Learning, 1–17.
Li, Y., Liu, Z., Zhao, J., Ren, L., Li, F., Luo, J., and Luo, B. (2024). The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking. arXiv preprint arXiv:2404.14581. https://doi.org/10.1007/978-3-031-70879-4_16
Liu, X., Kang, J., Ma, H., and Wang, C. (2021). Comparison Between Architects and Non-Architects on Perceptions of Architectural Acoustic Environments. Applied Acoustics, 184, 108313. https://doi.org/10.1016/j.apacoust.2021.108313
Mohamed, N. A. G., and Sadek, M. R. (2023). Artificial Intelligence as a Pedagogical Tool for Architectural Education: What Does the Empirical Evidence Tell Us? MSA Engineering Journal, 2, 133–148. https://doi.org/10.21608/msaeng.2023.291867
Png, W. H., Aun, Y., and Gan, M. (2024). FeaST: Feature-Guided Style Transfer for High-Fidelity Art Synthesis. Computers and Graphics, 122, 103975. https://doi.org/10.1016/j.cag.2024.103975
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv Preprint arXiv:2204.06125.
Sheela, M. A. A., Amulya, K., Lokesh, D., Yesubabu, K., and Ajay, P. (2025). Federated Multimodal Language Recognition: A Deep Learning Approach for Real-Time Applications. International Journal of Recent Advances in Engineering and Technology, 14(1), 17–26.
Short, C. E., and Short, J. C. (2023). The Artificially Intelligent Entrepreneur: ChatGPT, Prompt Engineering, and Entrepreneurial Rhetoric Creation. Journal of Business Venturing Insights, 19, e00388. https://doi.org/10.1016/j.jbvi.2023.e00388
Vartiainen, H., and Tedre, M. (2023). Using Artificial Intelligence in Craft Education: Crafting with Text-to-Image Generative Models. Digital Creativity, 34, 1–21. https://doi.org/10.1080/14626268.2023.2174557
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al. (2022). Emergent Abilities of Large Language Models. Transactions on Machine Learning Research, 2022.
Xu, Y., Xu, X., Gao, H., and Xiao, F. (2024). SGDM: An Adaptive Style-Guided Diffusion Model for Personalized Text to Image Generation. IEEE Transactions on Multimedia, 26, 9804–9813. https://doi.org/10.1109/TMM.2024.3399075
Zhu, M., Chen, H., Yan, Q., Huang, X., Lin, G., Li, W., Tuv, Z., Hu, H., Hu, J., and Wang, Y. (2023). GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image. arXiv preprint arXiv:2306.0857. https://doi.org/10.52202/075280-3398
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Copyright (c) 2026 Swati Shivkumar Shriyal, Dr. E. Senthil Kumaran, Dr. Divya Mishra, Ayush Gandhi, Mr. Yagna B. Adhyaru, Saraswati B, Srimathi N

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