A Survey on Face-Swapping Methods for Identity Manipulation in Deepfake Applications

Abstract

A face-swapping framework is designed to generate an image or video that merges the pose and characteristics of the input image with the identity from the source image. It has found significant applications in entertainment, privacy protection and digital content creation. However, this process is inherently complex, involving challenges like identity preservation, expression consistency and photorealism. Despite the rapid advancements in face-swapping technology, there has been a noticeable lack of in-depth analysis of the intricate mechanisms and recent developments in this field. This work attempts to bridge that gap by providing an extensive overview of face-swapping methods based on deep learning. Researchers, developers and practitioners interested in learning about the state of face-swapping technology and its possible uses may find this survey to be an invaluable resource. It will provide insights that can inform future research and innovation in this fast-evolving area.