Survey on Deep Fake Detection using Deep Learning

Abstract

The rise of deep fake technology poses significant challenges to the authenticity and integrity of visual content on digital platforms. This paper presents the development of a web application aimed at detecting deep fake images and videos through the implementation of advanced deep learning models. Leveraging research in the field of deep fake detection, this application integrates state-of-the-art algorithm like CNNs for accurate classification tasks. Key features of the application include a user-friendly interface allowing for the upload and analysis of images and videos, leveraging the trained models to detect potential deep fakes. Additionally, the web application incorporates a sophisticated detection methods to enhance accuracy and reliability. The deployment of the web application on popular platforms aims to provide users with a tool to verify the authenticity of visual content, thereby mitigating the potential negative consequences of deep fake manipulation. Continuous monitoring, updates, and adaptation to emerging deep fake techniques are prioritized to ensure the web applications effectiveness and relevance in an ever-evolving landscape of digital content manipulation