| dc.description.abstract | The rapid advancements in Artificial Intelligence and Deep Learning have revolutionized
synthetic media creation, with Deepfakes emerging as a significant societal threat. These
manipulated images and videos, often generated using Generative Adversarial Networks
(GANs) and Diffusion Models (DMs), now turn out increasingly realistic due to modern
technologies, making their detection more challenging and critical. Deepfakes cause
serious risks to privacy, cybersecurity, and the spread of misinformation, especially
across platforms like social media, where their rapid dissemination serves to undermine
trust and public discourse. This systematic review, conducted in line with the PRISMA
framework, examines over 50 research papers on Deepfake detection methods, focused
on Convolutional Neural Networks (CNNs), frequency-based approaches, and hybrid
models. The findings admit that CNNs excel in controlled settings, providing high
detection accuracy, but poor generalization when applied to diverse and evolving
Deepfake datasets. Hybrid models, while more adaptable to new manipulations, face
significant limitations due to high computational costs, impeding their scalability
for real-time applications. This study underscores the critical need for robust and
scalable detection systems that are capable of performing well in real-time to support
applications like social media moderation, cybersecurity defenses, and misinformation
prevention. The insights aim to guide future research toward developing versatile
and high-accuracy detection frameworks to neutralize the escalating threats posed by
increasingly realistic Deepfakes. | en_US |
