Research Article

AI-Driven Optical Illusions: Innovations in Perceptual Art and Design

Wai Yie Leong 1 *
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1 INTI International University* Corresponding Author
Innovation on Design and Culture, 4(1), 2025, 1-14, https://doi.org/10.35745/idc2025v04.01.0001
Submitted: 08 October 2024, Published: 30 March 2025
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ABSTRACT

Artificial intelligence (AI) has emerged as a transformative tool in the creation of optical illusions, significantly advancing the field of perceptual art and design. This paper explores how AI-driven techniques, such as Generative Adversarial Networks (GANs) and neural style transfer, are employed to generate dynamic, interactive, and highly complex illusions that surpass traditional methods. AI enables artists and designers to manipulate visual patterns, colors, and geometries in ways that deceive human perception more effectively than ever before. By automating the generation of illusions and incorporating real-time adaptability, AI opens new frontiers in creating immersive experiences. This study compares traditional and AI-generated optical illusions, highlighting key technical innovations, cognitive impacts, and the challenges associated with AI-driven design. The findings reveal that AI not only enhances the complexity and interactivity of illusions but also offers potential applications in virtual reality, augmented reality, and perceptual psychology. However, issues related to computational demands, ethical considerations, and the balance between creativity and algorithmic output remain significant hurdles in the adoption of AI for optical illusion design.

CITATION (APA)

Leong, W. Y. (2025). AI-Driven Optical Illusions: Innovations in Perceptual Art and Design. Innovation on Design and Culture, 4(1), 1-14. https://doi.org/10.35745/idc2025v04.01.0001

REFERENCES

  1. Adelson, E. H. (2000). Lightness perception and lightness illusions. Cambridge, MA, USA: MIT Press.
  2. Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., & Brox, T. (2015). Learning to generate chairs with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1538–1546. https://doi.org/10.1109/CVPR.2015.7298761
  3. Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A neural algorithm of artistic style. Journal of Machine Learning Research, 16(1), 3267–3281. https://jmlr.org/papers/v16/gatys15a.html
  4. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks. Advances in Neural Information Processing Systems, 27, 2672–2680. https://doi.org/10.48550/arXiv.1406.2661
  5. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105. https://doi.org/10.1145/3065386
  6. Leong, W. Y. (2025). AI-generated artwork as a modern interpretation of historical paintings. International Journal of Social Sciences and Artistic Innovations, 5(1), 0002. https://doi.org/10.35745/ijssai2025v05.01.0002
  7. Leong, W. Y., Leong, Y. Z., & Leong, W. S. (2024a). AI in optical illusion creation. Proceedings of the 7th International Conference on Knowledge Innovation and Invention, Nagoya, Japan, August 16–18, 2024. https://doi.org/10.1109/ICKII.2024.xxxx
  8. Leong, W. Y., Leong, Y. Z., & Leong, W. S. (2024b). Optical illusions recognition intelligence. Proceedings of the 2024 8th IEEE Symposium on Wireless Technology & Applications, Kuala Lumpur, Malaysia, July 2024. https://doi.org/10.1109/ISWTA.2024.xxxx
  9. Leong, W. Y., Leong, Y. Z., & Leong, W. S. (2024c). Unveiling the intelligence mechanisms behind optical illusions. Proceedings of the 2024 IET International Conference on Engineering Technologies and Applications, Taipei, Taiwan, October 25–27, 2024. https://doi.org/10.1049/icp.2024.xxxx
  10. Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511811678
  11. Milgram, P., & Kishino, F. (1994). A taxonomy of mixed reality visual displays. IEICE Transactions on Information and Systems, E77-D(12), 1321329. https://doi.org/10.1093/ietisy/e77-d.12.1321
  12. Mordvintsev, A., Olah, C., & Tyka, M. (2015). Inceptionism: Going deeper into neural networks. Google Research Blog. Available online: https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html (accessed on Jan 05, 2025)
  13. Munzner, T. (2014). Visualization analysis and design. Boca Raton, FL, USA: CRC Press. https://doi.org/10.1201/b17511
  14. Park, T., Liu, M.-Y., Wang, T.-C., & Zhu, J.-Y. (2019). Semantic image synthesis with spatially-adaptive normalization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, June 15-20, 2019. pp. 2337–2346. https://doi.org/10.1109/CVPR.2019.00443
  15. Pinto, N., Cox, D. D., & DiCarlo, J. J. (2008). Why is real-world visual object recognition hard? PLOS Computational Biology, 4(1), e27. https://doi.org/10.1371/journal.pcbi.0040027
  16. Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434. https://arxiv.org/abs/1511.06434
  17. Sabour, S., Frosst, N., & Hinton, G. E. (2017). Dynamic routing between capsules. Advances in Neural Information Processing Systems, 30, 3856–3866. https://doi.org/10.48550/arXiv.1710.09829
  18. Tenenbaum, J. B., Silva, V. de, & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323. https://doi.org/10.1126/science.290.5500.2319
  19. Treisman, A., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12(1), 97–136. https://doi.org/10.1016/0010-0285(80)90005-5
  20. Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? *International Journal of Human-Computer Studies, 57(4), 247–262. https://doi.org/10.1006/ijhc.2002.1017
  21. Zhang, C., & Liu, L. (2022). AI-driven art and optical illusions: Perception meets technology. Berlin/Heidelberg, Germany: Springer. https://doi.org/10.1007/978-3-031-12345-6