3066-280XISSN (Online)
Volume 5 , Issue 1 , PP: 44-65, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Al-Seyday.T. Qenawy 1 *
Doi: https://doi.org/10.54216/MOR.050103
The emergence of artificial intelligence has transformed the landscape of digital security, communication, and media authenticity. Among its most consequential manifestations are Deepfakes, hyper-realistic synthetic media that undermine trust, destabilize communication ecosystems, and challenge legal and ethical frameworks. This study presents a comprehensive synthesis of methodological contributions across domains such as cybersecurity, communication networks, social media governance, digital forensics, and abuse detection. By organizing the literature into distinct categories, the research highlights how artificial intelligence operates as both the generator of risk and the foundation for its mitigation. Methodological trajectories include conceptual surveys of dual-use AI in cybersecurity, ensemble models for fraud detection, adaptive frameworks for phishing prevention, federated learning for privacy-preserving analytics, and the integration of AI with IoTenabled communication systems. Furthermore, interdisciplinary approaches extend the scope of detection and governance into educational, psychological, and social contexts, demonstrating that the challenge is not solely technical but systemic. The findings underscore recurring themes of hybridization, interpretability, resilience, and ethical responsibility, revealing that the future of AI-based defense mechanisms lies in their capacity to integrate technical rigor with human-centered and institutional perspectives. Ultimately, this review positions Deepfake detection and related AI applications within a wider constellation of methodological innovation, emphasizing that the problem of synthetic deception cannot be resolved through isolated technical solutions but requires coordinated, adaptive, and ethically grounded strategies capable of evolving alongside adversarial threats.
Deepfake detection , Artificial intelligence methodologies , Cybersecurity , Communication systems , Digital forensics
[1] Ruchira Purohit, Yana Sane, Devashree Vaishampayan, Sowmya Vedantam, and Mangal Singh. AI vs. Human vision: A comparative analysis for distinguishing AI-generated and natural images. In 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), pages 1–7. IEEE, 2024.
[2] Shavez Mushtaq Qureshi, Atif Saeed, Sultan H. Almotiri, Farooq Ahmad, and Mohammed A. Al Ghamdi. Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media. PeerJ Computer Science, 10:e2037, 2024. Publisher: PeerJ Inc.
[3] Diya Garg and Rupali Gill. A Bibliometric Analysis of Deepfakes: Trends, Applications and Challenges. EAI Endorsed Transactions on Scalable Information Systems, 11(6), 2024.
[4] E. Kodhai and B. Harishwar. Emerging Threats to Personal Data: AI-Powered Cyberattacks. In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI), pages 1–6. IEEE, 2025.
[5] Suchitra Patnaik and Santosh Kumar Biswal. Use of artificial intelligence and blockchain technologies in detecting and curbing fake news in journalism. In AI-Based Metaheuristics for Information Security and Digital Media, pages 1–18. Chapman and Hall/CRC, 2023.
[6] Santosh Kumar Biswal, Ambika Sankar Mishra, and Narsingh Majhi. Use of technologies in media and communication: Interventions of artificial intelligence in mitigating fake news on social media. In AI-Based Metaheuristics for Information Security and Digital Media, pages 95–111. Chapman and Hall/CRC, 2023.
[7] Sandeep M. Jadhav, Rachana V. Mahule, and Aditya P. Sontakke. Advances in Cybersecurity: A Comprehensive Review of Emerging Threats and Defense Mechanisms. In EPJ Web of Conferences, volume 328, page 01047. EDP Sciences, 2025.
[8] Doddi Srilatha and N. Thillaiarasu. A novel intelligent-based intrusion detection and prevention system in the cloud using deep learning with meta-heuristic strategy. International Journal of Data Mining and Bioinformatics, 29(3):241–277, 2025. Publisher: Inderscience Publishers (IEL).
[9] Anders Kristian Munk, Mathieu Jacomy, Matilde Ficozzi, and Torben Elgaard Jensen. Beyond artificial intelligence controversies: What are algorithms doing in the scientific literature? Big Data & Society, 11(3):20539517241255107, September 2024.
[10] Maryam Munawar, Iram Noreen, Raed S. Alharthi, and Nadeem Sarwar. Forged Video Detection Using Deep Learning: A SLR. Applied Computational Intelligence and Soft Computing, 2023:1–21, October 2023.
[11] Muhammad Jibreel Sammar, Muhammad Anwaar Saeed, Syed Muhammad Mohsin, Syed Muhammad Abrar Akber, Rasool Bukhsh, Mohammed Abazeed, and Mohammed Ali. Illuminating the future: A comprehensive review of AI-based solar irradiance prediction models. IEEE Access, 12:114394–114415, 2024. Publisher: IEEE.
[12] Neha Pramanick, Jimson Mathew, Shitharth Selvarajan, and Mayank Agarwal. Leveraging stacking machine learning models and optimization for improved cyberattack detection. Scientific Reports, 15(1):16757, 2025. Publisher: Nature Publishing Group UK London.
[13] M. Swarna Sudha, S. Manjula, Indira Bharathi, Valarmathi Krishnasamy, and K. Vijayalakshmi. DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI. In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL), pages 1293–1299. IEEE, 2025.
[14] Rohish Shatanand Angawalkar. Securing people against media generative AI-Educative approach towards generative AI. PhD Thesis, Dublin, National College of Ireland, 2024.
[15] Abiodun Abdullahi Solanke. Digital forensics AI: On practicality, optimality, and interpretability of digital evidence mining techniques. 2022. Publisher: alma.
[16] David Camacho, Juan Gómez-Romero, and Jason J. Jung. Special issue on infodemics. Journal of Ambient Intelligence and Humanized Computing, 15(3):1975–1980, March 2024.
[17] Bushra Alhijawi, Rawan Jarrar, Aseel AbuAlRub, and Arwa Bader. Deep learning detection method for large language models-generated scientific content. Neural Computing and Applications, 37(1):91–104, January 2025.
[18] Abid Iqbal, Khurram Shahzad, Shakeel Ahmad Khan, and Muhammad Shahzad Chaudhry. The relationship of artificial intelligence (AI) with fake news detection (FND): a systematic literature review. Global Knowledge, Memory and Communication, 74(5-6):1617–1637, 2025. Publisher: Emerald Publishing Limited.
[19] Mulahuwaish Aos, Basheer Qolomany, Kevin Gyorick, Jacques Bou Abdo, Mohammed Aledhari, Junaid Qadir, Kathleen Carley, and Ala Al-Fuqaha. A survey of social cybersecurity: Techniques for attack detection, evaluations, challenges, and future prospects. 2025. Publisher: Elsevier.
[20] Fatma S. Alrayes, Mohammed Maray, Asma Alshuhail, Khaled Mohamad Almustafa, Abdulbasit A. Darem, Ali M. Al-Sharafi, and Shoayee Dlaim Alotaibi. Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment. Scientific reports, 15(1):3338, 2025. Publisher: Nature Publishing Group UK London.
[21] Jianhua Ma, Qun Jin, Hui-Huang Hsu, John Paul C. Vergara, Antonio Guerrieri, Claudio Miceli, and Ao Guo. Challenges and Emerging Issues for Generative AI and Hyper Intelligence. In 2024 IEEE Cyber Science and Technology Congress (CyberSciTech), pages 258–265. IEEE, 2024.
[22] Md Fazley Rafy. Artificial intelligence in cyber security. Available at SSRN 4687831, 2024.
[23] Ioannis Syllaidopoulos, Klimis Ntalianis, and Ioannis Salmon. A Comprehensive Survey on AI in Counter-Terrorism and Cybersecurity: Challenges and Ethical Dimensions. IEEE Access, 2025. Publisher: IEEE.
[24] A. Deenu Mol, P. Revathi, V. Swetha, and U. Vimalan. Enhancing Fraud Detection with Boosting Models and Weighted Ensemble Learning. In 2025 Second International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS), pages 276–281. IEEE, 2025.
[25] C. Rajeswari. Artificial Intelligence in Phishing Detection and Analysis: A Foundational Review. In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI), pages 1–5. IEEE, 2025.
[26] Rahul Haripriya, Nilay Khare, Manish Pandey, and Sreemoyee Biswas. A privacy-enhanced framework for collaborative Big Data analysis in healthcare using adaptive federated learning aggregation. Journal of Big Data, 12(1):113, May 2025.
[27] Guma Ali, Aziku Samuel, Maad M. Mijwil, Kholoud Al-Mahzoum, Malik Sallam, Ayodeji Olalekan Salau, Indu Bala, Klodian Dhoska, and Engin Melekoglu. Enhancing Cybersecurity in Smart Education with Deep Learning and Computer Vision: A Survey. Mesopotamian Journal of Computer Science, 2025:115–158, 2025.
[28] Azwar MQ Agha. Artificial Intelligence in Social Media: Opportunities and Perspectives. Cihan University-Erbil Journal of Humanities and Social Sciences, 9(1):125–132, 2025. Publisher: Cihan University-Erbil.
[29] Zhao Haoran and Chen Zixuan. From Cyberbullying to Chronic Stress: AI-Driven Approaches for Identifying Abuse and Its Consequences. Available at SSRN 5362129, 2025.
[30] Abdalbasit Qadir, Bryar A. Hassan, and Hozan Khalid. Large-scale deep learning based face recognition utilizing vector database technologies: current trends, challenges, and solutions. International Journal of Computers and Applications, pages 1–23, July 2025.
[31] Fang Xu, Touseef Hussain, Manzoor Ahmed, Khurshed Ali, Muhammad Ayzed Mirza, Wali Ullah Khan, Asim Ihsan, and Zhu Han. The state of AI-empowered backscatter communications: A comprehensive survey. IEEE Internet of Things Journal, 10(24):21763–21786, 2023. Publisher: IEEE.
[32] Andriy Luntovskyy. How AI Meets Networking and Networks Meet AI Applications. In Andriy Luntovskyy, Mikhailo Klymash, Igor Melnyk, Mykola Beshley, and Alexander Schill, editors, Digital Ecosystems: Interconnecting Advanced Networks with AI Applications, volume 1198, pages 56–80. Springer Nature Switzerland, Cham, 2024. Series Title: Lecture Notes in Electrical Engineering.
[33] Zhen Xue. Enhancing phishing detection through machine learning. 2024.
[34] Ali Asghar, Amna Shifa, and Mamoona Naveed Asghar. Survey on Video Security: Examining Threats, Challenges, and Future Trends. Computers, Materials & Continua, 80(3), 2024.
[35] P. Zhou, X. Han, V. Morariu, and L. Davis. Deepfake detection using capsule networks and long shortterm memory. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 1–9, 2022.
[36] T. Mittal, S. Bhattacharya, and S. Chhabra. Explainable deepfake detection: Interpretable attention-based cnn models. Pattern Recognition Letters, 168:15–27, 2023.
[37] H. Nguyen, J. Yamagishi, and I. Echizen. Adversarial robustness in deepfake detection: A survey and new perspectives. ACM Computing Surveys, 56(3):1–38, 2024.
[38] R. Sunil. Exploring autonomous methods for deepfake detection. Journal of Digital Forensics and AI Security, 17(1):45–68, 2025.
[39] R. Ramanaharan. Deepfake video detection: Insights into model generalization. Signal Processing Advances, 29:120–143, 2025.
[40] M. Alrashoud. Deepfake video detection methods, approaches, and trends: A comprehensive survey. Multimedia Tools and Applications, 84(25):32501–32528, 2025.
