DEEP FAKE FACE DETECTION USING DEEP LEARNING

Abstract

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 and financial 
scams 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. These capabilities 
are used to train a ResNext which learns to categorizeif a video has been concern to 
manipulation or now no longer and is also capable of hit upon the temporal 
inconsistencies among frames presented by DF introduction tools.