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Computational aesthetics in dystopian visualization: an integrated approach using python programming and adobe photoshop's generative features
Abstract
This study aims to investigate how fundamental geometric shapes and digital tools can be integrated to create visually compelling representations of dystopian and post-apocalyptic themes, focusing on expanding the boundaries of digital art by combining traditional artistic practices with algorithmic design methods. Using Python programming and Adobe Photoshop's "Generative Image" feature (Beta version 25.11), five fundamental geometric shapes were generated and transformed into thematic visualizations through a two-phase methodological approach. Initially, the shapes were designed using Python's Matplotlib and NumPy libraries and programmed with algorithms containing random variables, establishing the foundation of structured randomness and algorithmic patterns controlled by the artist. Subsequently, these base shapes were enhanced and restructured through Photoshop's advanced generative tools, guided by specific thematic keywords such as "dystopian pattern," "post-apocalyptic scenario," and "hopelessness," resulting in a total of fifteen visuals comprising three variations for each geometric shape. The findings highlight the effective integration of basic design principles with advanced generative technologies, resulting in visually striking artworks that encapsulate dystopian aesthetics while effectively reflecting themes of isolation, decay, and technological domination through elements such as chaotic urban landscapes, fragmented architectures, and alien world terrains. This research contributes to existing work in algorithmic design and digital visualization while being associated with theoretical frameworks such as Jean Baudrillard's concept of hyperreality, Donna Haraway's union of human-machine-nature, and Walter Benjamin's critiques of modern urban life, demonstrating that generative art functions not only as an aesthetic tool but also as a platform for social and philosophical criticism, illustrating how art evolves into new narrative forms in the digital age and suggesting its capacity to expand artistic boundaries and redefine modes of expression.
Keywords
Supporting Institution
No funding support was received.
Ethical Statement
This research does not require ethical committee approval. There is no conflict of interest in the study. All authors have contributed equally to the preparation and writing of this article.
References
- Baudrillard, J. (1994). Simulacra and simulation. University of Michigan Press.
- Benjamin, W. (1968). The work of art in the age of mechanical reproduction. In H. Arendt (Ed.), Illuminations: Essays and reflections (pp. 217–251).
- Benjamin, W. (1999). The arcades project. Harvard University Press.
- Boden, M., & Edmonds, E. (2009). What is generative art? Digital Creativity, 20(1), 21–46. https://doi.org/10.1080/14626260902867915
- Chauhan, A., Chauhan, R., Nainwal, A., Arora, A., & Bhatt, C. (2023, September 14–16). Image multidiffusion algorithms for AI generative art. 2023 6th International Conference on Contemporary Computing and Informatics (IC3I).
- Christie’s. (2018). The AI art at Christie’s: How one painting generated more than $432,500. https://www.christies.com/en/stories/a-collaboration-between-two-artists-one-human-one-a-machine- 0cd01f4e232f4279a525a446d60d4cd1
- Elgammal, A. (2017). CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms. arXiv Preprint, arXiv:1706.07068. https://arxiv.org/abs/1706.07068
- Freeman, J. (2003). MetaMix: A symbiosis of familiar content with generative form [Conference presentation]. Generative Art Conference, Milan, Italy.
Details
Primary Language
English
Subjects
Image and Video Coding, Visual Arts (Other), Stage Design
Journal Section
Research Article
Early Pub Date
September 2, 2025
Publication Date
September 30, 2025
Submission Date
June 5, 2025
Acceptance Date
August 17, 2025
Published in Issue
Year 2025 Volume: 6 Number: 3
APA
Bebek, O., Kırboğa, K. K., & Coşar, M. (2025). Computational aesthetics in dystopian visualization: an integrated approach using python programming and adobe photoshop’s generative features. Journal for the Interdisciplinary Art and Education, 6(3), 211-224. https://doi.org/10.5281/zenodo.15971532
AMA
1.Bebek O, Kırboğa KK, Coşar M. Computational aesthetics in dystopian visualization: an integrated approach using python programming and adobe photoshop’s generative features. JIAE. 2025;6(3):211-224. doi:10.5281/zenodo.15971532
Chicago
Bebek, Ozan, Kevser Kübra Kırboğa, and Mehmet Coşar. 2025. “Computational Aesthetics in Dystopian Visualization: An Integrated Approach Using Python Programming and Adobe Photoshop’s Generative Features”. Journal for the Interdisciplinary Art and Education 6 (3): 211-24. https://doi.org/10.5281/zenodo.15971532.
EndNote
Bebek O, Kırboğa KK, Coşar M (September 1, 2025) Computational aesthetics in dystopian visualization: an integrated approach using python programming and adobe photoshop’s generative features. Journal for the Interdisciplinary Art and Education 6 3 211–224.
IEEE
[1]O. Bebek, K. K. Kırboğa, and M. Coşar, “Computational aesthetics in dystopian visualization: an integrated approach using python programming and adobe photoshop’s generative features”, JIAE, vol. 6, no. 3, pp. 211–224, Sept. 2025, doi: 10.5281/zenodo.15971532.
ISNAD
Bebek, Ozan - Kırboğa, Kevser Kübra - Coşar, Mehmet. “Computational Aesthetics in Dystopian Visualization: An Integrated Approach Using Python Programming and Adobe Photoshop’s Generative Features”. Journal for the Interdisciplinary Art and Education 6/3 (September 1, 2025): 211-224. https://doi.org/10.5281/zenodo.15971532.
JAMA
1.Bebek O, Kırboğa KK, Coşar M. Computational aesthetics in dystopian visualization: an integrated approach using python programming and adobe photoshop’s generative features. JIAE. 2025;6:211–224.
MLA
Bebek, Ozan, et al. “Computational Aesthetics in Dystopian Visualization: An Integrated Approach Using Python Programming and Adobe Photoshop’s Generative Features”. Journal for the Interdisciplinary Art and Education, vol. 6, no. 3, Sept. 2025, pp. 211-24, doi:10.5281/zenodo.15971532.
Vancouver
1.Ozan Bebek, Kevser Kübra Kırboğa, Mehmet Coşar. Computational aesthetics in dystopian visualization: an integrated approach using python programming and adobe photoshop’s generative features. JIAE. 2025 Sep. 1;6(3):211-24. doi:10.5281/zenodo.15971532