DeepDefend: A Robust Approach to DeepFake Detection

DeepDefend: A Robust Approach to DeepFake Detection

S. Sravani, B. Sai Varun Reddy, M. Sanjay, G. Sai Vardhan, Prof. P.Anjaiah

School of Engineering

MallaReddy University

Abstract: Expense Over the last few decades, rapid progress in AI, machine learning, and deep learning have led to the rise of highly realistic AI generated fake videos, these videos are commonly known as Deepfakes. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people. This project focuses on developing an effective deep fake detection system to counter the rising challenges associated with misinformation and manipulation. Leveraging advanced neural network architectures and extensive datasets comprising both authentic and synthetic content, the proposed solution aims to distinguish between genuine and manipulated media. The project employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract intricate features and temporal dependencies, enhancing the model's ability to discern subtle anomalies in the content. The project contributes to the ongoing efforts in deepfake detection, providing a comprehensive solution that combines the strengths of multiple algorithms to safeguard the integrity of digital media in an era of increasing manipulation challenges.