Deepfake on face and expression swap: a review.

Abstract

Remarkable advances have been made in deep learning, leading to the emergence of highly realistic AI-generated videos known as deepfakes. Deepfakes use generative models to manipulate facial features to create modified identities or expressions with impressive realism. These synthetic media creations can deceive, discredit, or blackmail individuals and threaten the integrity of the legal, political, and social systems. Consequently, researchers are actively developing techniques to detect deepfake content to preserve privacy and combat the dissemination of fabricated media. This article presents a comprehensive study examining existing methods of creating deepfake images and videos for face and expression replacement. In addition, it provides an overview of publicly available deepfake datasets for benchmarking, serving as important resources for training and evaluating deepfake detection systems. In addition, the study sheds light on the detection approaches used to identify deepfake face and expression swaps while discussing the challenges and issues involved. However, the focus of this study goes beyond identifying the existing barriers. It goes a step further by outlining future research directions and guiding future scientists to address the concerns that need to be addressed in deepfake detection methods. In this way, this paper aims to facilitate the development of robust and effective deepfake detection solutions for face and expression swaps, thereby contributing to ongoing efforts to protect the authenticity and trustworthiness of visual media.