DEEPFAKE TECHNOLOGY: THREAT LANDSCAPE, DETECTION TECHNIQUES, AND ETHICAL GOVERNANCE

Authors

  • abbaz primbetov tatu
  • Anvarjon Normuminov
  • Zulxumor Abdiraimova

Keywords:

: Deepfake, Generative Adversarial Networks (GANs), Cybersecurity, Deepfake Detection.

Abstract

Deepfake technology, driven by advancements in artificial intelligence (AI) and deep learning, facilitates the creation of hyper-realistic synthetic media—encompassing images, videos, and audio. While this innovation holds promise in fields such as entertainment, education, and digital accessibility, it simultaneously poses significant threats, including misinformation dissemination, identity theft, and cybersecurity vulnerabilities. The accelerating sophistication of deepfake generation methods underscores the urgent need for effective detection techniques and comprehensive regulatory responses. This paper explores the fundamental principles underlying deepfake creation, evaluates its associated risks, examines state-of-the-art detection strategies, and discusses future directions for promoting the ethical and secure application of deepfake technology.

References

References

Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018). MesoNet: A Compact Facial Video Forgery Detection Network. 2018 IEEE International Workshop on Information Forensics and Security (WIFS), 1–7. https://doi.org/10.1109/WIFS.2018.8630761

Choi, Y., Choi, M., Kim, M., Ha, J. W., Kim, S., & Choo, J. (2018). StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 8789–8797. https://doi.org/10.1109/CVPR.2018.00916

Ciftci, U. A., Demir, I., & Yin, L. (2020). FakeCatcher: Detection of Synthetic Portrait Videos Using Biological Signals. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2020.3012035

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27.

Karras, T., Laine, S., & Aila, T. (2019). A Style-Based Generator Architecture for Generative Adversarial Networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4401–4410. https://doi.org/10.1109/CVPR.2019.00453

Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1312.6114

Li, Y., Chang, M. C., & Lyu, S. (2018). In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking. 2018 IEEE International Workshop on Information Forensics and Security (WIFS), 1–7. https://doi.org/10.1109/WIFS.2018.8630761

Mirsky, Y., & Lee, W. (2021). The Creation and Detection of Deepfakes: A Survey. ACM Computing Surveys (CSUR), 54(1), 1–41. https://doi.org/10.1145/3390096

Verdoliva, L. (2020). Media Forensics and DeepFakes: An Overview. IEEE Journal of Selected Topics in Signal Processing, 14(5), 910–932. https://doi.org/10.1109/JSTSP.2020.2992344

Westerlund, M. (2019). The Emergence of Deepfake Technology: A Review. Technology Innovation Management Review, 9(11), 40–53. https://doi.org/10.22215/timreview/1282

Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2223–2232. https://doi.org/10.1109/ICCV.2017.244

Published

2025-06-03

How to Cite

primbetov, abbaz, Normuminov, A., & Abdiraimova, Z. (2025). DEEPFAKE TECHNOLOGY: THREAT LANDSCAPE, DETECTION TECHNIQUES, AND ETHICAL GOVERNANCE. The Descendants of Al-Fargani, (2), 106–112. Retrieved from http://al-fargoniy.uz/index.php/journal/article/view/838

Most read articles by the same author(s)