Deepfake Image Detection Using Deep Learning Approach: A survey

Abstract

The rapid advancements in artificial intelligence (AI) have brought forth deepfake technologies, leveraging sophisticated deep learning algorithms to generate highly realistic yet deceptive media. This poses a substantial threat to individuals’ integrity, privacy, and security, and can lead to widespread social and political instability. In response, there is an imperative necessity to create advanced computer models capable of efficiently identifying counterfeit content in real-time and notifying consumers of potential manipulations This paper presents a comprehensive examination of recent studies on deepfake detection utilizing deep learning techniques. Our aim is to advance the forefront by systematically categorizing the diverse techniques employed for identifying counterfeit content. Furthermore, we outline the merits and drawbacks of each approach and propose many directions for future research to address the persistent challenges and shortcomings in deepfake content identification.