Comparative Analysis of Deep Learning Techniques for Deepfake Detection: Evaluating Threats and Opportunities

Deepfake, a term combining "deep learning" and "fake," is a growing concern in various fields due to the ability to generate realistic images and visuals using advanced visual effects and AI algorithms. This study aims to critically analyze various deep learning approaches for deepfake detection, focusing on their performance, robustness, and adaptability. The re-search will evaluate techniques like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Generative Adversarial Networks (GANs), and The Convolutional Vision Transformer (CVT). The results will highlight the efficiency of ensemble modeling based on combined models, highlighting the strengths of multiple models. Additionally, the research undertakes a comprehensive literature review that analyzes several research works done by other researchers in field of deepfake detection. The research gaps demonstrate where further research and analysis is required, thereby revealing the weaknesses and unresolved challenges that face deepfake de-tection.