Paper Title
A Unified System for Multi-Factor Deepfake Detection

Abstract
Deepfake technology has advanced significantly in recent years, raising concerns regarding the authenticity of visual media. As the proliferation of deepfake videos poses risks to privacy, security, and information integrity, the need for effective detection methods has become critical. This project explores innovative approaches to deepfake face detection, utilizing machine learning algorithms and computer vision techniques. We propose a multi-faceted framework that employs convolutional neural networks (CNNs) and feature extraction methods to accurately identify manipulated images. Our methodology includes training the model on diverse datasets to enhance its robustness against various deepfake generation techniques. We evaluate our model’s performance through extensive testing, comparing it with existing state-of-the-art detection systems. The results indicate a significant improvement in detection accuracy, demonstrating the efficacy of our approach. This research contributes to developing reliable tools for combating misinformation and enhancing the credibility of digital media.