Deep Fake Detection Using Deep Learning

Abstract

This project tackles the increasing threat posed by deepfake technology, which enables the creation of highly convincing but deceptive images and videos. Deepfakes can spread misinformation and compromise digital trust, making effective detection methods crucial. Our solution is a comprehensive deepfake detection system designed to analyze both image and video content with high accuracy. For video analysis, we utilize Gated Recurrent Units (GRUs), a type of recurrent neural network well-suited for modeling temporal sequences. GRUs capture subtle temporal patterns and inconsistencies across video frames that often indicate manipulation. For images, we employ Convolutional Neural Networks (CNNs), focusing on two well-known architectures: VGG16 and MobileNet. These CNNs excel at extracting detailed visual features, enabling reliable classification of images as either genuine or deepfakes. To make the detection accessible and user-friendly, we implement a web application where users can upload their images or videos for instant analysis and receive classification results in real time. This combination of temporal modeling with GRUs and spatial feature extraction through CNNs offers a robust approach to detecting sophisticated deepfake content. Our system is designed not only for accuracy but also for ease of use, aiming to empower individuals and organizations to verify digital media authenticity effectively. Ultimately, this project contributes toward mitigating the impact of deepfakes, helping to preserve the integrity and trustworthiness of digital media in an era where manipulation techniques are rapidly evolving.