Deep fake Detection using deep learning techniques: A Literature Review

TL;DR: In this paper , a study comprehensively evaluates deep fake production and detection technologies based on several deep learning algorithms, and the limits of current approaches and the availability of databases in society will be discussed.

Abstract: Deep learning is a sophisticated and adaptable technique that has found widespread use in fields such as natural language processing, machine learning, and computer vision. It is one of the most recent deep learning-powered applications to emerge. Deep fakes are altered, high-quality, realistic videos/images that have lately gained popularity. Many incredible uses of this technology are being investigated. Malicious uses of fake videos, such as fake news, celebrity pornographic videos, financial scams, and revenge porn are currently on the rise in the digital world. As a result, celebrities, politicians, and other well-known persons are particularly vulnerable to the Deep fake detection challenge. Numerous research has been undertaken in recent years to understand how deep fakes function and many deep learning-based algorithms to detect deep fake videos or pictures have been presented.This study comprehensively evaluates deep fake production and detection technologies based on several deep learning algorithms. In addition, the limits of current approaches and the availability of databases in society will be discussed. A deep fake detection system that is both precise and automatic. Given the ease with which deep fake videos/images may be generated and shared, the lack of an effective deep fake detection system creates a serious problem for the world. However, there have been various attempts to address this issue, and deep learning-related solutions outperform traditional approaches.