Deep Fake Technology in Media: A literature review
Keywords:
Deepfake technology, Artificial intelligence, Identity theft, AI ethicsAbstract
With rapid advances in artificial intelligence (AI), deepfake technology can create highly realistic visual and audio media using deep learning and generative adversarial networks (GANs). While beneficial in entertainment, education, and media production, it raises ethical, social, and security concerns. This report outlines its evolution, technical mechanisms, and growing role in misinformation and disinformation. It examines misuse in identity theft, political manipulation, propaganda, and defamation, and their impact on trust. It also reviews countermeasures such as AI detection, watermarking, regulation, and awareness. A cross-disciplinary approach is essential to balance innovation with safeguards and ensure responsible use.
References
Abdulhamed, M. A., & Hashim, A. N. (2024). A survey on detecting deep fakes using advanced AI-based approaches. Iraqi Journal of Science.Chandra, N. A., et al. (2025). Deepfake-Eval-2024:
A multimodal in-the-wild benchmark of deepfakes. arXiv Preprint. Edwards, P., Nebel, J.-C., Greenhill, D., & Liang, X. (2024).
A review of deepfake techniques: Architecture, detection and datasets. IEEE Access.
Goodfellow, I., et al. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems (pp. 2672–2680).
Gong, L. Y., & Li, X. J. (2024). A contemporary survey on deepfake detection: Datasets, algorithms, and challenges. Electronics, 13(3).
Güera, D., & Delp, E. J. (2018). Deepfake video detection using recurrent neural networks. In Proceedings of the IEEE International Workshop on Information Forensics and Security.
Khan, S. A., & Dang-Nguyen, D.-T. (2023). Deepfake detection: A comparative analysis. arXiv Preprint.
Liu, P., Tao, Q., & Zhou, J. T. (2024). Evolving from single-modal to multi-modal facial deepfake detection: A survey. arXiv Preprint.
Lu, Y., & Ebrahimi, T. (2024). Assessment framework for deepfake detection in real-world situations. EURASIP Journal on Image and Video Processing.
Mirsky, Y., & Lee, W. (2021). The creation and detection of deepfakes: A survey. ACM Computing Surveys, 54(1), 1–41.
Singh, S., Srivastav, S., Singh, S., & Hussain, S. (2025). Deepfake detection systems: A comprehensive survey of algorithms and techniques.
Verdoliva, L. (2020). Media forensics and deepfakes: An overview. IEEE Journal of Selected Topics in Signal Processing, 14(5), 910–932.
Doss, C., Mondschein, J., Shu, D., Wolfson, T., Kopecky, D., Fitton-Kane, V. A., Bush, L., & Tucker, C. (2023). Deepfakes and scientific knowledge dissemination. Scientific Reports, 13, 13439. https://doi.org/10.1038/s41598-023-39944-3
Gao, L., Ahmed, A., Chen, O., Reyl, M., Cheema, Z., Feamster, N., Tan, C., Thomas, K., & Chetty, M. (2026). Governance of AI-generated content: A case study on social media platforms. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26). ACM. https://doi.org/10.1145/3772318.3790415
Hwang, Y., & Ryu, J. Y. (2021). Effects of disinformation using deepfake: The protective effect of media literacy education. Cyberpsychology, Behavior, and Social Networking, 24(3), 184–192. https://doi.org/10.1089/cyber.2020.0174
Khan, S. A., & Dai, H. (2021). Video transformer for deepfake detection with incremental learning. In Proceedings of the 29th ACM International Conference on Multimedia (MM ’21) (pp. 1821–1829). ACM. https://doi.org/10.1145/3474085.3475332
Kulangareth, N. V., Kaufman, J., Oreskovic, J., & Fossat, Y. (2024). Investigation of deepfake voice detection using speech pause patterns: Algorithm development and validation. JMIR Biomedical Engineering, 9, e56245. https://doi.org/10.2196/56245
Man, Q., & Cho, Y.-I. (2025). Exposing face manipulation based on generative adversarial network–transformer and fake frequency noise traces. Sensors, 25(5), 1435. https://doi.org/10.3390/s25051435
Ming, L. (2025). A review of deepfake and its detection: From generative adversarial networks to diffusion models. International Journal of Intelligent Systems, 2025, 9987535. https://doi.org/10.1155/int/9987535
Moyo, B. V. C., & Obagbuwa, I. C. (2026). An AI-driven conceptual framework for detecting fake news and deepfake content: A systematic review. Frontiers in Artificial Intelligence, 9. https://doi.org/10.3389/frai.2026.1737790
Pawelec, M. (2022). Deepfakes and democracy (theory): How synthetic audio-visual media for disinformation and hate speech threaten core democratic functions. Digital Society, 1(3), 19. https://doi.org/10.1007/s44206-022-00010-6
Piazza, A., & Voltolini, F. (2023). Designed to abuse? Deepfakes and the non-consensual diffusion of intimate images. Synthese, 200(2), 144. https://doi.org/10.1007/s11229-022-04012-2
Romero Moreno, F. (2024). Generative AI and deepfakes: A human rights approach to tackling harmful content. International Review of Law, Computers & Technology, 38(3), 219–242. https://doi.org/10.1080/13600869.2024.2324540
Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. (2019). FaceForensics++: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1–11). IEEE. https://doi.org/10.1109/ICCV.2019.00009
Schiavone, G., & Maulini, G. (2025). Deepfake detection in generative AI: A legal framework proposal to protect human rights. Computer Law & Security Review, 57, 106121. https://doi.org/10.1016/j.clsr.2025.106121
Singh, S., & Dhumane, A. (2025). Unmasking digital deceptions: An integrative review of deepfake detection, multimedia forensics, and cybersecurity challenges. MethodsX, 2025, 103632. https://doi.org/10.1016/j.mex.2025.103632
Snauffer, A., Nyst, C., & Bursens, P. (2024). Non-consensual synthetic intimate imagery: Prevalence, attitudes, and knowledge in 10 countries. arXiv Preprint. https://doi.org/10.48550/arXiv.2402.01721
Twomey, J., Ching, D., Aylett, M. P., Quayle, M., Linehan, C., & Murphy, G. (2023). Do deepfake videos undermine our epistemic trust? A thematic analysis of tweets that discuss deepfakes in the Russian invasion of Ukraine. PLOS ONE, 18(10), e0291668. https://doi.org/10.1371/journal.pone.0291668
Vaccari, C., & Chadwick, A. (2020). Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media + Society, 6(1). https://doi.org/10.1177/2056305120903408
Dolhansky, B., et al. (2020). The deepfake detection challenge (DFDC) dataset. arXiv Preprint. https://doi.org/10.48550/arXiv.2006.07397
Geng, C. (2023). Comparing deepfake regulatory approaches. Georgetown Law Technology Review, 7, 157–184.
Aribe, S. (2025). Generative artificial intelligence and the evolving challenge of deepfake detection: A systematic analysis. Journal of Sensor and Actuator Networks, 14(1), 17. https://doi.org/10.3390/jsan14010017
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 MOSES MUKIIBI, Lyimo Johnson Samwel Lyimo , Gombe Mikael Tengemano, Ahmed Abdulhakim Al-Absi, Baseem Al Athwari
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
