Beyond Illusions: Contribution of Artificial Intelligence in Unveiling and Mitigating Deep Fake Impact on Social Networks


  • Virender Dhiman


Deepfake, AI-based mitigation, disinformation, privacy, detection algorithms, multimodal, multidisciplinary cooperation, media literacy


This article investigates the effects of deepfake technology on social networks and evaluates AI-based mitigating strategies. Deepfakes, or synthetic media created by AI, present concerns such as misrepresentation and privacy breaches. Deepfake detection, Face Manipulation Detection Networks (FMDNs) and multimodal analysis are the important AI techniques. Real-world implementations demonstrate less deepfake diffusion. Future research will focus on model interpretability, multidisciplinary cooperation, and media literacy in order to effectively mitigate deepfakes. Ethical issues are critical in addressing emerging challenges.


Download data is not yet available.


Kietzmann, J., Lee, L.W., McCarthy, I.P. and Kietzmann, T.C., 2020. Deepfakes: Trick or treat?. Business Horizons, 63(2), pp.135-146.

Montasari, R., 2024. The Dual Role of Artificial Intelligence in Online Disinformation: A Critical Analysis. In Cyberspace, Cyberterrorism and the International Security in the Fourth Industrial Revolution: Threats, Assessment and Responses (pp. 229-240). Cham: Springer International Publishing.

Stroebel, L., Llewellyn, M., Hartley, T., Ip, T.S. and Ahmed, M., 2023. A systematic literature review on the effectiveness of deepfake detection techniques. Journal of Cyber Security Technology, 7(2), pp.83-113.

Montasari, R., 2024. Responding to Deepfake Challenges in the United Kingdom: Legal and Technical Insights with Recommendations. In Cyberspace, Cyberterrorism and the International Security in the Fourth Industrial Revolution: Threats, Assessment and Responses (pp. 241-258). Cham: Springer International Publishing.

Ferrara, E., 2024. GenAI against humanity: Nefarious applications of generative artificial intelligence and large language models. Journal of Computational Social Science, pp.1-21.

Helmus, T.C., 2022. Artificial Intelligence, Deepfakes, and Disinformation.

Wagner, T.L. and Blewer, A., 2019. “The word real is no longer real”: Deepfakes, gender, and the challenges of ai-altered video. Open Information Science, 3(1), pp.32-46.

Fletcher, J., 2018. Deepfakes, Artificial Intelligence, and Some Kind of Dystopia. Theatre Journal, 70(4), pp.455-471.

Mubarak, R., Alsboui, T., Alshaikh, O., Inuwa-Dute, I., Khan, S. and Parkinson, S., 2023. A Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats. IEEE Access.

Köbis, N.C., Doležalová, B. and Soraperra, I., 2021. Fooled twice: People cannot detect deepfakes but think they can. Iscience, 24(11).

Kim, B., Xiong, A., Lee, D. and Han, K., 2021. A systematic review on fake news research through the lens of news creation and consumption: Research efforts, challenges, and future directions. PloS one, 16(12), p.e0260080.

Li, M. and Wan, Y., 2023. Norms or fun? The influence of ethical concerns and perceived enjoyment on the regulation of deepfake information. Internet Research, 33(5), pp.1750-1773.

Ahmed, S.R., Sonuç, E., Ahmed, M.R. and Duru, A.D., 2022, June. Analysis survey on deepfake detection and recognition with convolutional neural networks. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-7). IEEE.

Remya Revi, K., Vidya, K.R. and Wilscy, M., 2021. Detection of deepfake images created using generative adversarial networks: A review. In Second International Conference on Networks and Advances in Computational Technologies: NetACT 19 (pp. 25-35). Springer International Publishing.

Rahman, A., Islam, M.M., Moon, M.J., Tasnim, T., Siddique, N., Shahiduzzaman, M. and Ahmed, S., 2022. A qualitative survey on deep learning based deep fake video creation and detection method. Aust. J. Eng. Innov. Technol, 4(1), pp.13-26.

Gocen, I., 2023. European Union's Approach to Artificial Intelligence in the Context of Human Rights (Doctoral dissertation, Dokuz Eylul Universitesi (Turkey)).

Barman, D., Guo, Z. and Conlan, O., 2024. The Dark Side of Language Models: Exploring the Potential of LLMs in Multimedia Disinformation Generation and Dissemination. Machine Learning with Applications, p.100545.

Chan, C.C.K., Kumar, V., Delaney, S. and Gochoo, M., 2020, September. Combating deepfakes: Multi-LSTM and blockchain as proof of authenticity for digital media. In 2020 IEEE/ITU International Conference on Artificial Intelligence for Good (AI4G) (pp. 55-62). IEEE.

Shamanth, M., Mathias, R. and MN, D.V., 2022. Detection of fake faces in videos. arXiv preprint arXiv:2201.12051.

Sidoti, O. and Vogels, E.A., 2023. What Americans Know About Al, Cybersecurity and Big Tech.

Passos, L.A., Jodas, D., Costa, K.A., Souza Júnior, L.A., Rodrigues, D., Del Ser, J., Camacho, D. and Papa, J.P., 2022. A review of deep learning‐based approaches for deepfake content detection. Expert Systems, p.e13570.

Muammar, S., Shehada, D. and Mansoor, W., 2023. Digital Risk Assessment Framework for Individuals: Analysis and Recommendations. IEEE Access.

Chen, J., Geng, Y., Chen, Z., Pan, J.Z., He, Y., Zhang, W., Horrocks, I. and Chen, H., 2023. Zero-shot and few-shot learning with knowledge graphs: A comprehensive survey. Proceedings of the IEEE.

Lomnitz, M., Hampel-Arias, Z., Sandesara, V. and Hu, S., 2020, October. Multimodal approach for deepfake detection. In 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) (pp. 1-9). IEEE.

Zhang, J., Li, J., Li, X.L., Shi, Y., Li, J. and Wang, Z., 2016. Domain-specific entity linking via fake named entity detection. In Database Systems for Advanced Applications: 21st International Conference, DASFAA 2016, Dallas, TX, USA, April 16-19, 2016, Proceedings, Part I 21 (pp. 101-116). Springer International Publishing.

Sánchez-Junquera, J., Villaseñor-Pineda, L., Montes-y-Gómez, M., Rosso, P. and Stamatatos, E., 2020. Masking domain-specific information for cross-domain deception detection. Pattern Recognition Letters, 135, pp.122-130.




How to Cite

Dhiman, V. . (2024). Beyond Illusions: Contribution of Artificial Intelligence in Unveiling and Mitigating Deep Fake Impact on Social Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2689–2698. Retrieved from



Research Article