DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning

TL;DR

This paper reviews current challenges, research trends, and future directions in DeepFake detection techniques, emphasizing the need for more robust methods to combat increasingly sophisticated synthetic media.

Contribution

It provides a comprehensive overview of DeepFake creation and detection research, highlighting gaps and proposing future research directions for improved robustness.

Findings

DeepFake technology is rapidly advancing, making detection more challenging.

Current detection methods need to evolve to handle more sophisticated DeepFake media.

Research trends indicate a focus on developing more resilient detection algorithms.

Abstract

The rapid advancement in deep learning makes the differentiation of authentic and manipulated facial images and video clips unprecedentedly harder. The underlying technology of manipulating facial appearances through deep generative approaches, enunciated as DeepFake that have emerged recently by promoting a vast number of malicious face manipulation applications. Subsequently, the need of other sort of techniques that can assess the integrity of digital visual content is indisputable to reduce the impact of the creations of DeepFake. A large body of research that are performed on DeepFake creation and detection create a scope of pushing each other beyond the current status. This study presents challenges, research trends, and directions related to DeepFake creation and detection techniques by reviewing the notable research in the DeepFake domain to facilitate the development of more robust approaches that could deal with the more advance DeepFake in the future.