Deepfake Detection Using Deep Learning: A Review

Abstract

Of late, social media has amplified rapidly, and deepfake information has cropped in. The Artificial Intelligence (AI) and data analytics have distorted enhancing program performance, which distracted dynamic, real-time insights and intrude risk assessments. But deepfake AI have altered social media that truncate the instantaneous decision-making processes, and mislead the strategic planning.  The present review focuses on insights blended approaches, highlighting real-time analytics and improved decision-making by differentiating between real-time data from fake interpretations. The proliferation of deepfakes across social media has raised significant concerns about misinformation, identity fraud, and public trust. Using advanced AI techniques such as Generative Adversarial Networks (GANs) and diffusion models, they often mimic human likenesses with high precision, making manual detection increasingly difficult. This review explores recent advancements in automated deepfake detection using deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, GANs and Diffusion Models. Each model's role in identifying manipulated visual and audio content is critically analysed. Detection methodologies span across spatial, temporal, and physiological signal analyses, employing hybrid frameworks for enhanced accuracy. The review also evaluates the effectiveness of publicly available datasets and real-time detection tools such as Sentinel, Sensity, and Fake-catcher. With the continuous evolution of generative models, this study underscores the need for interpretable detection systems to safeguard digital authenticity.