AI ile Reklam Afişi Otomasyonuna Yönelik Güncel Sistemler
Keywords:
Reklam Afişi, Otomasyon, Yapay Zekâ, Dijital Tasarım, Autoposter, POSTA, AI Grafik TasarımAbstract
Bu çalışma, reklam afişi tasarımında yapay zekânın [YZ] giderek artan rolünü ve bu teknolojinin
getirdiği inovatif otomasyon potansiyelini kapsamlı biçimde incelemektedir. Özellikle son dönemde
geliştirilen AutoPoster ve POSTA gibi YZ tabanlı sistemler, kullanıcı girdilerine dayalı olarak afiş
tasarım sürecinde görsel düzen, tipografi, renk uyumu, metin ve kompozisyon gibi öznitelikleri otomatik
biçimde üretebilme yeteneğine sahiptir. Bu sistemler, tasarım süresini önemli ölçüde kısaltmakta, üretim
maliyetlerini azaltmakta ve aynı zamanda profesyonel düzeyde estetik sonuçlar elde edilmesini mümkün
kılmaktadır. Literatürde “AI-assisted creative automation” olarak adlandırılan bu yaklaşım, insan
yaratıcılığı ile makine öğrenmesinin etkileşimini yeniden tanımlamakta; tasarımcıların yönlendirici, seçici
ve denetleyici roller üstlenmesini teşvik etmektedir. Bununla birlikte, çalışmada şeffaf algoritmaların
kullanımı, kullanıcı kontrolünün korunması, özgünlük ve etik tasarım ilkelerinin önemi de
vurgulanmaktadır. Araştırma, yapay zekâ destekli afiş otomasyonunun yalnızca teknik bir kolaylık değil,
aynı zamanda yeni bir yaratıcı paradigma sunduğunu öne sürmektedir. Sonuç olarak, bu çalışma,
gelecekte YZ’nin reklam ve grafik tasarımı alanlarında yaratıcı süreçleri nasıl dönüştürebileceğine, insanmerkezli tasarım değerlerini koruyarak nasıl sürdürülebilir bir inovasyon zemini oluşturabileceğine dair
öngörüler sunmaktadır.
Downloads
References
Alebachew, A. [2025]. The influence of AI and automation on modern graphic design. Journal of Creative Technology. https://www.researchgate.net/publication/389349895
Anantrasirichai, N., & Bull, D. [2020]. Artificial intelligence in the creative industries: A review. arXiv preprint, arXiv:2007.12391. https://arxiv.org/abs/2007.12391
Chung, J., Lee, Y., & Park, S. [2022]. AI in Social Media Marketing: Enhancing Visual Campaign Effectiveness. Journal of Interactive Advertising, 22[1], 33–49. https://doi.org/10.1080/15252019.2022.2110986
Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. [2017]. CAN: Creative Adversarial Networks, Generating “Art” by Learning About Styles and Deviating from Style Norms. arXiv:1706.07068. https://doi.org/10.48550/arXiv.1706.07068
Kaur, R., & Joshi, P. [2022]. Adaptive Poster Generation in Gaming Ecosystems Using Generative Models. Entertainment Computing, 42, 100476. https://doi.org/10.1016/j.entcom.2022.100476
Kim, D., & Kim, H. [2022]. Democratizing Design: AI Tools for Small Enterprises. Design Management Review, 33[2], 42–53.
Li, Y., Chen, H., & Wang, Z. [2024]. Poster Artistic Automation: Towards Personalized Visual Composition via Multimodal Diffusion. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition [CVPR]. https://doi.org/10.1109/CVPR57402.2024
Lin, J., Zhou, M., Ma, Y., Gao, Y., Fei, C., Chen, Y., Yu, Z., & Ge, T. [2023]. AutoPoster: A highly automatic and content-aware design system for advertising poster generation. arXiv preprint, arXiv:2308.01095. https://arxiv.org/abs/2308.01095
Lin, Y. H., Lobo, A., & Leckie, C. [2017]. Green Advertising Effectiveness: Examining the Relationships between Advertisement Characteristics, Attitude, and Green Purchase Intentions. International Journal of Advertising, 36[2], 250–271. https://doi.org/10.1080/02650487.2016.1193063
Lu, X., Wang, Z., & Yang, H. [2020]. Poster Generation with Latent Variable Generative Models. Proceedings of the AAAI Conference on Artificial Intelligence, 34[02], 1346–1353. https://doi.org/10.1609/aaai.v34i02.5527
Ma, L., Liu, Y., Wang, Z., Gu, J., Zhang, L., & Zhu, Y. [2023]. Text2Poster: Compositional and Controllable Poster Generation from Natural Language. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9874–9883.
Mangini, A., Ragazzi, A., & Viola, L. [2020]. Green Marketing, Consumer Confusion and Greenwashing: A Field Experiment. Sustainability, 12[18], 7486. https://doi.org/10.3390/su12187486
Manovich, L. [2020]. Cultural analytics. The MIT Press.
Martínez, P. [2015]. Customer Loyalty: Effects of Trust and Satisfaction in Green Marketing. Management Decision, 53[3], 572–589. https://doi.org/10.1108/MD-09-2013-0514
McCormack, J., Gifford, T., & Hutchings, P. [2019]. Autonomy, Authenticity, Authorship and Intention in Computer Generated Art. In D. Collins [Ed.], The Oxford Handbook of Algorithmic Music. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190226992.013.35
Mu, S., & Lee, H. [2023]. CSR and Greenwashing: Employee Perceptions and Internal Trust. Corporate Communications: An International Journal, 28[1], 77–95.
Nyilasy, G., Gangadharbatla, H., & Paladino, A. [2013]. Greenwashing: A Consumer Perspective. International Journal of Advertising, 32[1], 113–134. https://doi.org/10.2501/IJA-32-1-113-134
Patil, A., Gupta, P., & Raina, R. [2024]. Green Marketing in the Age of Artificial Intelligence: Authenticity and Consumer Trust. Journal of Marketing Technology, 9[2], 43–59.
Pham, T. N., Phung, D. D., & Nguyen, L. T. [2024]. Trust Recovery Strategies in the Wake of Greenwashing: Evidence from Eco-Friendly Services. Journal of Environmental Psychology, 89, 102042.
Pieters, R., Wedel, M., & Batra, R. [2010]. The stopping power of advertising: Measures and effects of visual complexity. Journal of Marketing, 74[5], 48–60. https://doi.org/10.1509/jmkg.74.5.48
Qayyum, A., Wang, C., & Javed, A. [2022]. Green Branding and the Effects of Greenwashing: A Comparative Study. Journal of Cleaner Production, 370, 133478. https://doi.org/10.1016/j.jclepro.2022.133478
Schmidt, J. A. [2021]. AI-generated Design and the Future of Creativity: How Automation is Shaping Graphic Design Practice. Design Issues, 37[3], 31–45. https://doi.org/10.1162/desi_a_00649
Seele, P., & Gatti, L. [2015]. Greenwashing Revisited: In Search of a Typology and Accusation-based Definition Incorporating Legitimacy Strategies. Business Strategy and the Environment, 26[2], 239–252. https://doi.org/10.1002/bse.1912
Sun, Q., Li, J., & Wang, Y. [2023]. Multimodal Generative Models for Automated Poster Design in Marketing. Journal of Visual Communication and Image Representation, 88, 103717. https://doi.org/10.1016/j.jvcir.2023.103717
Teichmann, K., Stokburger‐Sauer, N. E., & Demetz, L. [2024]. CSR Communication and Consumer Cynicism: The Moderating Role of Brand Sincerity. Corporate Social Responsibility and Environmental Management, 31[1], 122–135.
Urbański, M., & Haque, A. U. [2020]. Are Consumers Willing to Pay More for Green Products? Empirical Insights from Emerging Markets. Sustainability, 12[24], 10263. https://doi.org/10.3390/su122410263
Wahyuni, R., & Zulfikar, M. [2024]. The Role of Perceived Authenticity in Consumer Trust Toward AI-Generated Content. Journal of Contemporary Marketing Research, 6[2], 28–39.
Wang, L., & Sun, J. [2023]. Emotional engagement and machine-created design: A comparative study. International Journal of Human–Computer Interaction, 39[8], 712–730. https://doi.org/10.1080/10447318.2023.2182176
Wang, L., & Zhao, M. [2021]. Event-based Poster Automation: A Real-Time AI Architecture. Proceedings of the 30th ACM Conference on Multimedia, 1132–1140.
Wang, S., & Walker, T. [2023]. Regaining Trust After Greenwashing: Strategic Communication and Crisis Management. Journal of Brand Strategy, 11[4], 383–399.
Zhang, Z., Cheng, Y., Hong, D., Yang, M., Shi, G., Ma, L., Zhang, H., Shao, J., & Wu, X. [2025]. CreatiPoster: Towards editable and controllable multi-layer graphic design generation. arXiv preprint, arXiv:2506.10890. https://arxiv.org/abs/2506.10890
Zhou, K., Liu, F., & Yuan, Q. [2022]. AI-generated advertising visuals and user perception: The uncanny valley of creativity. Visual Communication Quarterly, 29[3], 187–200. https://doi.org/10.1080/15551393.2022.2100000