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.
