A Survey of Deep Fake Detection for Trial Courts

TL;DR

This survey reviews current DeepFake detection methods and datasets, emphasizing the importance of identifying manipulated media to prevent misinformation, and discusses recent research trends in the field.

Contribution

It provides a comprehensive overview of DeepFake detection techniques, datasets, and research trends, highlighting the state-of-the-art and future directions.

Findings

Extensive review of DeepFake detection methods

Summary of available datasets for DeepFake detection

Discussion of emerging research trends in the field

Abstract

Recently, image manipulation has achieved rapid growth due to the advancement of sophisticated image editing tools. A recent surge of generated fake imagery and videos using neural networks is DeepFake. DeepFake algorithms can create fake images and videos that humans cannot distinguish from authentic ones. (GANs) have been extensively used for creating realistic images without accessing the original images. Therefore, it is become essential to detect fake videos to avoid spreading false information. This paper presents a survey of methods used to detect DeepFakes and datasets available for detecting DeepFakes in the literature to date. We present extensive discussions and research trends related to DeepFake technologies.