A Review on Deepfake generation and Detection based on Deep learning: Approaches, and Future Challenges

Abstract

In recent years, applications of deepfake, particularly to achieve political, economic, or social reputation aims, have been become widespread. These applications do not require high-level professional technical skills. Also, deep learning techniques like Generative Adversarial networks (GANs) have enhanced deepfake, making it more realistic. So, several researchers are looking for developing an effective method to detect a fake image or video. This paper provides a comprehensive overview of several proposed deepfake generation approaches and the approaches used to detect any manipulation. Based on feature extraction methods, this study provides an extensive review of face manipulation, especially focusing on facial swap, re-enactment, and attribute manipulation. Additionally, the study describes all existing deepfake methods and evaluates the presented detection models based on the most effective deep learning algorithms by comparing their respective evaluation metrics. Moreover, it presents the challenges and gapes in trying to enhance and develop deepfake detection techniques. It assists readers in understanding the generation and detection of deepfake mechanisms and presents the field limitations and future works.