Citation

Jahan, Fariha and Shifat, Sirajum Munira and Morol, Md. Kishor and Fahad, Nafiz and Goh, Kah Ong Michael and Nandi, Dip and Abdullah-Al-Jubair, Md. and Hossen, Md. Jakir (2026) Generative AI and the rise of deepfake technology: a journey from traditional techniques to LLM integration. Discover Applied Sciences, 8 (5). ISSN 3004-9261

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Abstract

Generative artificial intelligence (AI) is advancing rapidly, with deepfake technology illustrating the intersection of AI and multimedia forensics. This study examines the classification and evolution of deepfake, exploring their definition, societal impact, advantages, challenges, and future potential. It traces the development of deepfake from early techniques to modern generative models and analyzes the role of large language models (LLMs) in their creation and detection. The study also emphasizes the emerging role of large vision-language models (LVLMs) in enhancing multimodal deepfake detection. By categorizing deepfake advancements and their enabling AI technologies, this research offers a structured framework for researchers and practitioners. Additionally, it highlights critical risks, including misinformation and the erosion of public trust. To address these concerns, the study advocates for a multidisciplinary mitigation strategy, combining technical, ethical, and policy-based approaches, along with a proposed prevention framework.

Item Type:	Article
Uncontrolled Keywords:	Generative AI, Deepfake, Large language models (LLMs), LLMs-driven deepfake, Large vision-language models (LVLMs), Multimodal deepfake detection, Ethical AI, Mitigation strategies
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	05 Jun 2026 07:13
Last Modified:	05 Jun 2026 07:13
URII:	http://shdl.mmu.edu.my/id/eprint/16052

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