Identification of Deepfakes using Strategic Models and Architectures

Abstract

Deepfake technology has been rapidly evolving and expanding in recent years. It has become increasingly easy to manipulate multimedia content, making it harder to detect what is real and what is manipulated. The research aims to explore how neural networks can be used to detect deepfake in multimedia, helping to protect users from potentially malicious and deceptive content. The aim is to explore what neural networks are, how they can be used to detect deepfakes and the potential implications of this technology. The research also aims to evaluate the advantages and disadvantages of using neural networks for deepfake detection. As the world of deepfake technology continues to evolve, this research will provide an overview of the latest developments in deepfake detection and their potential impact. The goal of this research is to use neural networks to detect deepfakes and to identify suspicious content to alert users. This could help protect users from being exposed to malicious content and help content producers ensure the integrity of their work. As deepfake technology continues to evolve, neural networks may become an essential tool for quickly and accurately detecting deepfakes in multimedia. The research explores topics like, CNN, 3D CNN, GATED RECURRENT UNIT and Architectures like Xception, VGG16, InceptionV3 and ResNet50V2. The outcomes are graphically represented and analyzed. the comparative stratification of the approach is done to analyze and detect deepfakes.