Exploring Deepfake Detection: Techniques, Datasets, and Challenges

Abstract

Deepfake detection is an active area of research due to extensive use of deepfake media for spreading false information, manipulate public opinion and cause harm to individuals. This paper presents a critical and systematic review of 84 articles for deepfake generation and detection. We review the current state-of-the-art techniques for deepfake detection techniques by grouping them into four different categories: deep learning-based techniques, traditional machine learning-based, artifacts analysis-based and biological signal-based methods, the datasets used for training and testing deepfake detection models. We also discuss the evaluation metrics used to measure the effectiveness of these methods and the challenges and future directions of deepfake detection research. Our findings suggest that deep learning models demonstrate superior accuracy compared to other methods and artifacts analysis-based methods shows greater potential in precision but there is still room for improvement in detecting more sophisticated and realistic deepfakes.