Originality of AI-Generated digital artworks: A systematic review of the literature
DOI:
https://doi.org/10.15649/2346030X.4582Keywords:
generative artificial intelligence, generative ia, originality, digital artworks, authenticityAbstract
This Systematic Literature Review article aimed to summarize information on the originality of digital artworks created by generative AI, for this purpose, a search was conducted in the following databases: IEEE Xplore, Scopus, ScienceDirect and SpringerLink, finding, after applying inclusion and exclusion criteria, a total of 51 research articles. The main findings were: the most outstanding contributions of generative AI in digital art were efficiency, automation and artistic innovation, with a valuation of 21.88%; the most used techniques for the creation of digital works were Antagonistic Generative Networks and their variants, with a valuation of 32. 14%; the most relevant ethical challenges are related to data bias and discrimination, reaching 32%; the most common training data were tagged images and public bases, with a rating of 32.5%; and finally, the key aspects to evaluate originality in these works were visual quality and diversity, with a rating of 30%. In conclusion, generative AI is key to innovation in digital art, but its use should be managed with caution due to the biases and ethical challenges involved.
References
[1] S. Bengesi, H. El-Sayed, M. K. Sarker, Y. Houkpati, J. Irungu, y T. Oladunni, "Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers", IEEE Access, vol. 12, pp. 69812-69837, may 2024, doi: 10.1109/ACCESS.2024.3397775.
[2] Z. W. Wu, H. Qu, y K. Zhang, "A Survey of Recent Practice of Artificial Life in Visual Art", Artif. Life, vol. 30, n.o 1, pp. 106-135, feb. 2024, doi: 10.1162/artl_a_00433.
[3] M. Gao y P. Pu, "Generative Adversarial Network-Based Experience Design for Visual Communication: An Innovative Exploration in Digital Media Arts", IEEE Access, vol. 12, pp. 92035-92042, jul. 2024, doi: 10.1109/ACCESS.2024.3419212.
[4] B. Kitchenham, P. Brereton, D. Budgen, M. Turner, J. Bailey, y S. Linkman, "Systematic literature reviews in software engineering-A systematic literature review", Inf. Softw. Technol., vol. 51, pp. 7-15, ene. 2009, doi: 10.1016/j.infsof.2008.09.009.
[5] L. Banh y G. Strobel, "Generative artificial intelligence", Electron. Mark., vol. 33, n.o 1, p. 63, dic. 2023, doi: 10.1007/s12525-023-00680-1.
[6] Y. Wang, Y. Pan, M. Yan, Z. Su, y T. H. Luan, "A Survey on ChatGPT: AI–Generated Contents, Challenges, and Solutions", IEEE Open J. Comput. Soc., vol. 4, pp. 280-302, oct. 2023, doi: 10.1109/OJCS.2023.3300321.
[7] A. Tian y L. Lu, "Attentional Generative Adversarial Networks With Representativeness and Diversity for Generating Text to Realistic Image", IEEE Access, vol. 8, pp. 9587-9596, ene. 2020, doi: 10.1109/ACCESS.2020.2964946.
[8] K. Tatar et al., "A Shift in Artistic Practices through Artificial Intelligence", Leonardo, vol. 57, n.o 3, pp. 293-297, 2024, doi: 10.1162/leon_a_02523.
[9] N. Anantrasirichai y D. Bull, "Artificial intelligence in the creative industries: a review", Artif. Intell. Rev., vol. 55, n.o 1, pp. 589-656, jul. 2022, doi: 10.1007/s10462-021-10039-7.
[10] T. Wu et al., "Recent advances in 3D Gaussian splatting", Comput. Vis. Media, vol. 10, n.o 4, pp. 613-642, ago. 2024, doi: 10.1007/s41095-024-0436-y.
[11] R. West, A. Burbano, y M. Tromble, "Ai, arts & design: Questioning learning machines", Artnodes, vol. 2020, n.o 26, pp. 1-9, jul. 2020, doi: 10.7238/a.v0i26.3368.
[12] C. Chen, "Study on the Innovative Development of Digital Media Art in the Context of Artificial Intelligence", Comput. Intell. Neurosci., vol. 2022, ago. 2022, doi: 10.1155/2022/1004204.
[13] Y. Shen y F. Yu, "The Influence of Artificial Intelligence on Art Design in the Digital Age", Sci. Program., vol. 2021, dic. 2021, doi: 10.1155/2021/4838957.
[14] M. P. Schofield, "Camera Phantasma: Reframing virtual photographies in the age of AI", Convergence, vol. 30, n.o 2, pp. 687-709, dic. 2023, doi: 10.1177/13548565231220314.
[15] I. Kalpokas, "Work of art in the Age of Its AI Reproduction", Philos. Soc. Crit., jun. 2023, doi: 10.1177/01914537231184490.
[16] Y. Lyu, X. Wang, R. Lin, y J. Wu, "Communication in Human–AI Co-Creation: Perceptual Analysis of Paintings Generated by Text-to-Image System", Appl. Sci. Switz., vol. 12, n.o 22, nov. 2022, doi: 10.3390/app122211312.
[17] M. Baas, "Artificial Intelligence and the question of creativity: Art, data and the sociocultural archive of AI-imaginations", Eur. J. Cult. Stud., vol. 27, n.o 4, pp. 788-795, may 2024, doi: 10.1177/13675494241246640.
[18] R. Latikka, J. Bergdahl, N. Savela, y A. Oksanen, "AI as an Artist? A Two-Wave Survey Study on Attitudes Toward Using Artificial Intelligence in Art", Poetics, vol. 101, nov. 2023, doi: 10.1016/j.poetic.2023.101839.
[19] K. Millet, F. Buehler, G. Du, y M. Kokkoris, "Defending humankind: Anthropocentric bias in the appreciation of AI art", Comput. Hum. Behav., vol. 143, p. 107707, feb. 2023, doi: 10.1016/j.chb.2023.107707.
[20] A. Bandi, P. V. S. R. Adapa, y Y. E. V. P. K. Kuchi, "The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges", Future Internet, vol. 15, n.o 8, Art. n.o 8, jul. 2023, doi: 10.3390/fi15080260.
[21] A. Aggarwal, M. Mittal, y G. Battineni, "Generative adversarial network: An overview of theory and applications", Int. J. Inf. Manag. Data Insights, vol. 1, n.o 1, p. 100004, abr. 2021, doi: 10.1016/j.jjimei.2020.100004.
[22] Y. Sun, Y. Lyu, P.-H. Lin, y R. Lin, "Comparison of Cognitive Differences of Artworks between Artist and Artistic Style Transfer", Appl. Sci. Switz., vol. 12, n.o 11, may 2022, doi: 10.3390/app12115525.
[23] K. M. Jang et al., "Place identity: a generative AI’s perspective", Humanit. Soc. Sci. Commun., vol. 11, n.o 1, sep. 2024, doi: 10.1057/s41599-024-03645-7.
[24] S. Göring, R. R. Ramachandra Rao, R. Merten, y A. Raake, "Analysis of Appeal for Realistic AI-Generated Photos", IEEE Access, vol. 11, pp. 38999-39012, abr. 2023, doi: 10.1109/ACCESS.2023.3267968.
[25] G. Devsana, K. Kumar, S. R, y S. T, "Automatic Creation of Quality Images from Text using Multiple Generative Adversial Network", Procedia Comput. Sci., vol. 230, pp. 955-963, ene. 2023, doi: 10.1016/j.procs.2023.12.135.
[26] G. Castellano y G. Vessio, "A Deep Learning Approach to Clustering Visual Arts", Int. J. Comput. Vis., vol. 130, n.o 11, pp. 2590-2605, ago. 2022, doi: 10.1007/s11263-022-01664-y.
[27] H. Pflüger, "A language to analyze, describe, and explore collections of visual art", Vis. Comput. Ind. Biomed. Art, vol. 4, n.o 1, p. 5, mar. 2021, doi: 10.1186/s42492-021-00071-3.
[28] D. B. Ebaid, M. M. Madbouly, y A. A. El-Zoghabi, "Bi-directional Image–Text Matching Deep Learning-Based Approaches: Concepts, Methodologies, Benchmarks and Challenges", Int. J. Comput. Intell. Syst., vol. 16, n.o 1, p. 81, may 2023, doi: 10.1007/s44196-023-00260-3.
[29] G. Castellano y G. Vessio, "Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview", Neural Comput. Appl., vol. 33, n.o 19, pp. 12263-12282, abr. 2021, doi: 10.1007/s00521-021-05893-z.
[30] K. Psychogyios, H. C. Leligou, F. Melissari, S. Bourou, Z. Anastasakis, y T. Zahariadis, "SAMStyler: Enhancing Visual Creativity With Neural Style Transfer and Segment Anything Model (SAM)", IEEE Access, vol. 11, pp. 100256-100267, sep. 2023, doi: 10.1109/ACCESS.2023.3315235.
[31] J. Oppenlaender, "A taxonomy of prompt modifiers for text-to-image generation", Behav. Inf. Technol., nov. 2023, doi: 10.1080/0144929X.2023.2286532.
[32] J. O’Meara y C. Murphy, "Aberrant AI creations: co-creating surrealist body horror using the DALL-E Mini text-to-image generator", Convergence, vol. 29, n.o 4, pp. 1070-1096, jul. 2023, doi: 10.1177/13548565231185865.
[33] Q. Wang, C. Guo, H.-N. Dai, y P. Li, "Stroke-GAN Painter: Learning to paint artworks using stroke-style generative adversarial networks", Comput. Vis. Media, vol. 9, n.o 4, pp. 787-806, dic. 2023, doi: 10.1007/s41095-022-0287-3.
[34] S. Sai, R. Sai, y V. Chamola, "Generative AI for Industry 5.0: Analyzing the impact of ChatGPT, DALLE, and Other Models", IEEE Open J. Commun. Soc., pp. 1-1, ene. 2024, doi: 10.1109/OJCOMS.2024.3400161.
[35] L. Wang, W. Chen, W. Yang, F. Bi, y F. R. Yu, "A State-of-the-Art Review on Image Synthesis with Generative Adversarial Networks", IEEE Access, vol. 8, pp. 63514-63537, abr. 2020, doi: 10.1109/ACCESS.2020.2982224.
[36] B. Kang, S. Tripathi, y T. Q. Nguyen, "Generating images in compressed domain using generative adversarial networks", IEEE Access, vol. 8, pp. 180977-180991, oct. 2020, doi: 10.1109/ACCESS.2020.3027800.
[37] B. Liu, J. Lv, X. Fan, J. Luo, y T. Zou, "Application of an Improved DCGAN for Image Generation", Mob. Inf. Syst., vol. 2022, jul. 2022, doi: 10.1155/2022/9005552.
[38] K. Ko, T. Yeom, y M. Lee, "SuperstarGAN: Generative adversarial networks for image-to-image translation in large-scale domains", Neural Netw., vol. 162, pp. 330-339, mar. 2023, doi: 10.1016/j.neunet.2023.02.042.
[39] L. Ren y Y. Song, "AOGAN: A generative adversarial network for screen space ambient occlusion", Comput. Vis. Media, vol. 8, n.o 3, pp. 483-494, sep. 2022, doi: 10.1007/s41095-021-0248-2.
[40] Z. Yang, Y. Chen, Z. Le, y Y. Ma, "GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks", Neural Comput. Appl., vol. 33, n.o 11, pp. 6133-6145, nov. 2020, doi: 10.1007/s00521-020-05387-4.
[41] D. Park, H. Na, y D. Choi, "Performance Comparison and Visualization of AI-Generated-Image Detection Methods", IEEE Access, vol. 12, pp. 62609-62627, may 2024, doi: 10.1109/ACCESS.2024.3394250.
[42] J. J. Bird y A. Lotfi, "CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images", IEEE Access, vol. 12, pp. 15642-15650, feb. 2024, doi: 10.1109/ACCESS.2024.3356122.
[43] S. K. Lee y Y. R. Koo, "Proposal of a Facilitation and Process Model for Enhancing Creativity in Co-design Workshops with Generative AI: The Use of ChatGPT", 31 de mayo de 2024. doi: 10.15187/adr.2024.05.37.2.249.
[44] F.-L. Chen et al., "VLP: A Survey on Vision-language Pre-training", Mach. Intell. Res., vol. 20, n.o 1, pp. 38-56, feb. 2023, doi: 10.1007/s11633-022-1369-5.
[45] J. Xu, M. Yuan, D.-M. Yan, y T. Wu, "Deep unfolding multi-scale regularizer network for image denoising", Comput. Vis. Media, vol. 9, n.o 2, pp. 335-350, jun. 2023, doi: 10.1007/s41095-022-0277-5.
[46] K. Zhang et al., "Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis", Mach. Intell. Res., vol. 20, n.o 6, pp. 822-836, dic. 2023, doi: 10.1007/s11633-023-1466-0.
[47] P. Li, D. Zhang, L. Zhao, D. Xu, y D. Lu, "Style Permutation for Diversified Arbitrary Style Transfer", IEEE Access, vol. 8, pp. 199147-199158, nov. 2020, doi: 10.1109/ACCESS.2020.3034653.
[48] H. Ramadan, C. Lachqar, y H. Tairi, "A survey of recent interactive image segmentation methods", Comput. Vis. Media, vol. 6, n.o 4, pp. 355-384, dic. 2020, doi: 10.1007/s41095-020-0177-5.
[49] E. J. López-Ortiz, M. Perea-Trigo, L. M. Soria-Morillo, F. Sancho-Caparrini, y J. J. Vegas-Olmos, "Exploring deep echo state networks for image classification: a multi-reservoir approach", Neural Comput. Appl., vol. 36, n.o 20, pp. 11901-11918, abr. 2024, doi: 10.1007/s00521-024-09656-4.
[50] T. Qin, S. Tu, y L. Xu, "IA-NGM: A bidirectional learning method for neural graph matching with feature fusion", Mach. Learn., vol. 113, n.o 4, pp. 1743-1769, nov. 2022, doi: 10.1007/s10994-022-06255-z.
[51] E. M. Kuehn, "A new business model in the fine arts realm based on NFT certificates and pearl codes", Digit. Bus., vol. 4, n.o 2, jun. 2024, doi: 10.1016/j.digbus.2024.100079.
[52] S. S. Gill y R. Kaur, "ChatGPT: Vision and challenges", Internet Things Cyber-Phys. Syst., vol. 3, pp. 262-271, may 2023, doi: 10.1016/j.iotcps.2023.05.004.
Downloads
Published
How to Cite
Downloads
Issue
Section
License
Copyright (c) 2025 AiBi Journal of Research, Administration and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
The journal offers open access under a Creative Commons Attibution License
This work is under license Creative Commons Attribution (CC BY 4.0).
