A comprehensive review of deepfakes in digital media forensics
DOI:
https://doi.org/10.53704/Keywords:
Cybersecurity, Vulnerability, AI-based Detection, , Multimodal Forensics, . Synthetic Media, Artificial IntelligenceAbstract
Deepfake technology has grown fast in recent years. It started as a simple experiment but is now widely used to create very realistic fake images, sounds, and videos. This development emerged from advanced Artificial Intelligence (AI) methods, including generative adversarial networks (GANs), autoencoders, and diffusion models. This paper examines how deepfakes are made, the tools behind them, and different ways people use them, both for good and bad. Ninety-five papers in all were gathered from various sources. After carefully reviewing each publication, it was found that some shared similar objectives and methods. As a result, the most pertinent papers were chosen for adoption and review. This article examines the challenges of detecting deepfakes using AI tools, including CNNs, time-based models, attention mechanisms, and multimodal models. The paper points out real problems with these detection systems, such as biased data, difficulty handling new kinds of deepfakes, and attacks that try to fool the detectors. In the end, it emphasises that fixing the deepfake problem is not just a technological issue. It also needs laws, better rules, and more public understanding.
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Copyright (c) 2026 NURENI AZEEZ, Damilola S Aaron, S. A. Akinbooro, Chioma C. Isiekwene
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