A Systematic Literature Review of Deepfake Face Image Detection with Transfer Learning Techniques

Abstract

Artificial intelligence-based fake content generation, also known as deepfake media content, is spreading rapidly with the advancement of technology. This can manipulate or completely change the real media by threatening the authenticity of the data. With the availability of major amounts of data, deepfake face image generation has become an emerging issue in today’s digitalized world. Therefore, it is crucial to understand the most effective methods to detect this deepfake face image content. This paper provides a comprehensive discussion about how transfer learning can be used in this specific area to detect deepfake face images through a systematic literature review of (49) papers using the PRISMA method. The paper addresses the gap in up-to-date detailed analysis of transfer learning methods in this area which emphasizes the effectiveness of the process of deepfake image detection by studying papers in the past decade. This paper presents a comprehensive discussion of the models that have been used in this area along with the transfer learning approaches that have been used. The key findings highlight that the most common transfer learning approach for deepfake image detection is CNN combined and the most effective model for deepfake image detection in transfer learning is fine-tuned models. This paper also highlights the challenges and possible improvements in the area of deepfake face image detection with transfer learning techniques.