DeepFake Detection Through Deep Learning: A Comprehensive Review

Abstract

Deepfake technology, driven by advances in deep learning and generative models such as Generative Adversarial Networks (GANs), has emerged as a significant threat to digital media authenticity. The increasing realism of manipulated images, videos, and audio has raised serious concerns in areas including social media, journalism, digital forensics, national security, and public trust. This review paper presents a comprehensive analysis of deep-fake detection techniques based on deep learning methodologies. The paper systematically explores convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, ensemble learning, multimodal frameworks, and lightweight real-time detection models. A detailed literature study highlights recent state-of-the-art approaches, performance metrics, advantages, and limitations. Comparative analyses are provided to evaluate detection accuracy, computational complexity, robustness, and generalization capability. Furthermore, existing research gaps and challenges such as dataset bias, generalization to unseen manipulations, real-time constraints, and adversarial robustness are discussed. The paper concludes by outlining future research directions emphasizing multimodal learning, explainable AI, domain adaptation, and ethical and regulatory considerations. This review aims to serve as a valuable reference for researchers and practitioners working in the field of deepfake detection.