Deepfake Detection Project

Overview

This project is a Django-based web application designed to detect deepfake videos. Utilizing advanced machine learning techniques such as Long Short-Term Memory (LSTM) networks and ResNeXt Convolutional Neural Networks (CNN), the application provides a robust solution for identifying manipulated videos.

Objective

The primary objective of this project is to develop a reliable and efficient tool to combat the spread of deepfake content. By leveraging state-of-the-art machine learning models, we aim to create a system that can accurately distinguish between authentic and manipulated videos.

Technologies Used

• Django: A high-level Python web framework that encourages rapid development and clean, pragmatic design.
• LSTM (Long Short-Term Memory): A type of recurrent neural network (RNN) capable of learning long-term dependencies, particularly useful in sequence prediction problems.
• ResNeXt CNN: A variant of the traditional Convolutional Neural Network that uses grouped convolutions to enhance performance and accuracy.

Features

• User Authentication: Secure login and registration system.
• Upload Videos: Users can upload videos to be analyzed for deepfake detection.
• Real-time Analysis: The system processes the uploaded videos and provides a detailed report on the likelihood of manipulation.
• Dashboard: A user-friendly interface to view analysis results and manage uploaded videos.

Usage

1. Register or log in to your account.
2. Upload a video for deepfake analysis.
3. View the results on the dashboard.

Model Training

The models used for deepfake detection are trained using the following steps:

1. Data Collection: Gather a dataset of authentic and deepfake videos.
2. Preprocessing: Convert videos into frames and preprocess the data.
3. Model Training: Train the LSTM and ResNeXt CNN models using the preprocessed data.
4. Evaluation: Evaluate the models' performance and fine-tune as necessary.
5. Integration: Integrate the trained models into the Django application.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -am 'Add your feature').
4. Push to the branch (git push origin feature/your-feature).
5. Create a new Pull Request.