Deepfake Detection and Prevention Using Deep Learning

Abstract

The rapid evolution of artificial intelligence technologies has greatly increased the capabilities of creating synthetic media content, also referred to as deepfakes. These artificially created images, audio recordings, and videos are able to convincingly replicate real people and events in the world. As a result, deepfake technology poses a serious concern when it comes to the authenticity and privacy of information. Even though deepfake technology is useful in the creation of positive content in the fields of entertainment, education, media production, and accessibility, its misuse in the spread of misinformation, identity theft, political manipulation, and defamation is a major concern. The present review paper aims to conduct a comprehensive review of the available research on deepfake generation and detection techniques. It also reviews the state-of-the-art deep learning models used in the creation of synthetic media content and the detection techniques used in the identification of deepfake content. In addition, the review also discusses the prevention and mitigation techniques used in the identification of deepfake content through the use of verification technologies, platforms, regulatory policies, and public awareness campaigns.