EVOLUTION OF DEEPFAKES AND THEIR CONSEQUENCES
DOI:
https://doi.org/10.65164/gzpqcm57Ключевые слова:
Deepfake, artificial intelligence (AI), deep learning, generative adversarial networks (GANs), synthetic media, image manipulation, face swapping.Аннотация
In recent years, the rapid development of artificial intelligence and deep learning technologies has significantly expanded the possibilities for creating and manipulating digital content. In this context, deepfake technology has emerged as a powerful tool capable of artificially modifying human faces, voices, and movements to generate highly realistic synthetic media. Although initially developed for entertainment and research purposes, deepfake technology has increasingly become associated with serious issues related to information security, political manipulation, fraud, and violations of personal privacy. This paper examines the origin, evolution, and advancement of deepfake technology based on modern artificial intelligence algorithms. The study analyzes the transition from traditional image manipulation techniques to advanced approaches utilizing Generative Adversarial Networks (GANs), autoencoders, convolutional neural networks (CNNs), and StyleGAN models. In addition, the paper discusses the widespread accessibility of deepfake creation tools, their social and political impact, and the growing risks associated with malicious use.
Библиографические ссылки
[1] Scheirer, W. (2023, June). Photoshop Fantasies. In Proceedings of the 2023 ACM Workshop
on Information Hiding and Multimedia Security (pp. 1–1).
[2] Wilson, K. (2022). Getting Started with Photoshop. In Introduction to Photoshop: An Essential
Guide for Absolute Beginners (pp. 1–21). Berkeley, CA: Apress.
[3] Iqbal, S. W., & Arora, B. (2022, May). Machine learning techniques for image manipulation
detection: A review and analysis. In The International Conference on Recent Innovations in
Computing (pp. 209–224). Singapore: Springer Nature Singapore.
[4] Rao, A. (2016). Photography in Hollywood: Image manipulation in modern entertainment.
Researchers World, 7(4), 167–172.
[5] Chandra, B. (2024). Building a Generative Adversarial Network for Image Synthesis. Indian
Scientific Journal of Research in Engineering and Management.
[6] Bhavani, V. (2024). Comprehensive Overview of GAN Technology: Architecture, Training,
and Applications. International Journal for Science Technology and Engineering.
[7] Bansal, G., Nawal, A., Chamola, V., & Herencsar, N. (2024). Revolutionizing visuals: The role
of generative AI in modern image generation. ACM Transactions on Multimedia Computing,
Communications and Applications, 20(11), 1–22.
[8] Sarpate, D., Mungi, A., Borkar, S., Mane, S., & Shaikh, K. (2024). A deep approach to deep
fake detection. International Journal of Scientific Research in Science, Engineering and
Technology, 11(2), 530–534.
[9] Septiawan, R. (2024). Critical analysis of AI-produced media: A study of the implications of
deepfake technology. Devotion: Journal of Research and Community Service, 5(7), 735–741.
[10] Wang, Y., Hao, Y., & Cong, A. X. (2024). Harnessing machine learning for discerning AIgenerated synthetic images. arXiv preprint arXiv:2401.07358.
[11] Diel, A., Lalgi, T., Schröter, I. C., MacDorman, K. F., Teufel, M., & Bäuerle, A. (2024).
Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers.
Computers in Human Behavior Reports, 16, 100538.
[12] Hoppe, D. (2021, March). Trends and emerging technologies in AI. In Sustained Simulation
Performance 2019 and 2020 (pp. 163–181). Cham: Springer International Publishing.171
[13] Chennupati, A. (2024). The evolution of AI: What does the future hold in the next two years.
World Journal of Advanced Engineering Technology and Sciences, 12(1), 022–028.
[14] Ateeq, A., Singh, A., Khan, A., Ahmed, M., & Hoque, I. (2024). Threat of deepfake enabled
attack.
[15] Lyu, S. (2024). Wrestling with the deepfakes: Detection and beyond. Open Access
Government.
[16] Gambín, Á. F., Yazidi, A., Vasilakos, A., Haugerud, H., & Djenouri, Y. (2024). Deepfakes:
Current and future trends. Artificial Intelligence Review, 57(3), 64.
[17] Amerini, I., Barni, M., Battiato, S., Bestagini, P., Boato, G., Bonaventura, T. S., et al. (2024,
September). Deepfake media forensics: State of the art and challenges ahead. In International
Conference on Advances in Social Networks Analysis and Mining (pp. 33–48). Cham: Springer
Nature Switzerland.
[18] Akhtar, Z., Pendyala, T. L., & Athmakuri, V. S. (2024). Video and audio deepfake datasets
and open issues in deepfake technology: Being ahead of the curve. Forensic Sciences, 4(3), 289–
377.
[19] Efremova, M. A., & Russkevich, E. A. (2024). Deepfake and criminal law. Bulletin of Kazan
Law Institute of the Ministry of Internal Affairs of Russia, 97.
[20] Alanazi, S., Asif, S., & Moulitsas, I. (2024). Examining the societal impact and legislative
requirements of deepfake technology: A comprehensive study.
[21] Talreja, S., Bindle, A., Kumar, V., Budhiraja, I., & Bhattacharya, P. (2024, June). Security
strengthen and detection of deepfake videos and images using deep learning techniques. In 2024
IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1834–
1839). IEEE.
[22] Tribhuvan, P. P., & Tribhuvan, A. P. (2024). Artificial intelligence for deepfake creation and
detection. In Futuristic Trends in Artificial Intelligence (Vol. 3, pp. 21–27).
[23] Mishra, A., Bharwaj, A., Yadav, A. K., Batra, K., & Mishra, N. (2024, January). Deepfakesgenerating synthetic images, and detecting artificially generated fake visuals using deep learning.
In 2024 14th International Conference on Cloud Computing, Data Science & Engineering
(Confluence) (pp. 587–592). IEEE.
[24] Seng, L. K., Mamat, N., Abas, H., & Ali, W. N. H. W. (2024). AI integrity solutions for
deepfake identification and prevention. Open International Journal of Informatics, 12(1), 35–46.
[25] Lorenzo, D. A. M. I. (2022). Analysis and conceptualization of deepfake technology as cyber
threat.
[26] Brooks, T., Princess, G., Heatley, J., Jeremy, J., & Scott, K. (2019). Increasing threats of
deepfake identities. US Department of Homeland Security.
[27] Popa, C., Pallath, R., Cunningham, L., Tahiri, H., Kesavarajah, A., & Wu, T. (2025).
Deepfake technology unveiled: The commoditization of AI and its impact on digital trust. arXiv
preprint arXiv:2506.07363.
