Media Forensics and Deepfake Systematic Survey

TL;DR

This survey comprehensively reviews Deepfake creation and detection methods, analyzing datasets, techniques, challenges, and recent developments to advance understanding and future research in media forensics.

Contribution

It provides an in-depth categorization of Deepfake techniques, evaluates datasets, and discusses recent progress and obstacles in detecting fake facial images.

Findings

Deepfake datasets have evolved significantly over time.

A general Deepfake detection model using deep learning is proposed.

Challenges remain in reliably detecting sophisticated Deepfakes.

Abstract

Deepfake is a generative deep learning algorithm that creates or changes facial features in a very realistic way making it hard to differentiate the real from the fake features It can be used to make movies look better as well as to spread false information by imitating famous people In this paper many different ways to make a Deepfake are explained analyzed and separated categorically Using Deepfake datasets models are trained and tested for reliability through experiments Deepfakes are a type of facial manipulation that allow people to change their entire faces identities attributes and expressions The trends in the available Deepfake datasets are also discussed with a focus on how they have changed Using Deep learning a general Deepfake detection model is made Moreover the problems in making and detecting Deepfakes are also mentioned As a result of this survey it is expected that the development of new Deepfake based imaging tools will speed up in the future This survey gives indepth review of methods for manipulating images of face and various techniques to spot altered face images Four types of facial manipulation are specifically discussed which are attribute manipulation expression swap entire face synthesis and identity swap Across every manipulation category we yield information on manipulation techniques significant benchmarks for technical evaluation of counterfeit detection techniques available public databases and a summary of the outcomes of all such analyses From all of the topics in the survey we focus on the most recent development of Deepfake showing its advances and obstacles in detecting fake images