A Machine Learning Approach to Deepfake Detection

The rapid rise of deepfake technology has led to growing concerns about its potential misuse in society. This review paper offers an in-depth analysis of recent advancements in deepfake detection through machine learning techniques. It explores different methods, datasets, challenges, and future research directions in this area. Convolutional Neural Networks (CNNs), known for their proficiency in capturing spatial features, and Graph Neural Networks (GNNs), which excel at understanding relational data, represent a significant breakthrough in developing more robust and complex deepfake detection systems. By integrating both spatial and relational information from multimedia content, these hybrid models enhance detection accuracy and provide a deeper understanding of the subtle alterations present in deepfake media. Through a thorough examination of prior studies, this paper highlights the advantages of hybrid models and explores their potential to address the complexities introduced by synthetic media manipulation. Notably, the combination of spatial and relational signals makes these models more resilient to adversarial attacks, enabling them to detect even the smallest inconsistencies introduced by deepfake techniques.