Hybrid Deep-Learning Model for Deepfake Detection in Video using Transfer Learning Approach

Abstract

Deepfake videos have become a growing concern in the digital age, presenting a substantial risk to the genuineness and trustworthiness of visual material. As these sophisticated manipulations continue to proliferate, there is a pressing need for advanced tools and techniques to detect and combat them effectively. In this article, we introduce a novel hybrid deep-learning model designed to enhance the accuracy of deepfake video detection using a Transfer Learning approach. Unlike traditional approaches, our hybrid model utilizes smart computer learning to carefully analyze videos for any signs of tampering. It's akin to having a digital detective to safeguard the truth of videos.