https://github.com/viraj7066/deepfake-detection-system

A deep learning-powered system for detecting deepfake videos using a ResNeXt + LSTM hybrid model with a Django web interface for real-time predictions.
https://github.com/viraj7066/deepfake-detection-system

ai-security deep-learning deepfake deepfake-detection fake-video-detection machine-learning

Last synced: 4 months ago
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A deep learning-powered system for detecting deepfake videos using a ResNeXt + LSTM hybrid model with a Django web interface for real-time predictions.

• Host: GitHub
• URL: https://github.com/viraj7066/deepfake-detection-system
• Owner: viraj7066
• License: gpl-3.0
• Created: 2025-05-13T10:58:48.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2025-05-13T12:13:34.000Z (5 months ago)
• Last Synced: 2025-05-13T13:24:07.399Z (5 months ago)
• Topics: ai-security, deep-learning, deepfake, deepfake-detection, fake-video-detection, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 72.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
Deepfake Detection System

Welcome to the Deepfake Detection System, a cutting-edge project designed to identify video deepfakes using advanced deep learning techniques. By combining ResNext for feature extraction and LSTM for sequence analysis, this project achieves high-accuracy detection of manipulated videos.

🚀 Features

Robust Deepfake Detection: Leverages a pretrained ResNext CNN and LSTM for accurate classification.

User-Friendly Web App: Built with Django, allowing users to upload videos and view results seamlessly.

Dockerized Deployment: Spin up the application effortlessly with Docker, no dependency hassles.

Detailed Documentation: Comprehensive guides to understand and replicate the project.

📂 Project Structure

Deepfake-Detection-System

├── Django Application    # Web interface for video uploads and predictions

├── Model Creation       # Scripts for building and training the model

├── Documentation        # In-depth project guides and resources

🛠️ System Architecture

The system extracts features from video frames using a pretrained ResNext model, which are then processed by an LSTM network to classify videos as real or fake.

⚙️ Installation

1. Clone the Repository

git clone https://github.com/viraj7066/Deepfake-Detection-System.git

cd Deepfake-Detection-System

2. Using Docker

Ensure Docker is installed.

Build and run the container:docker build -t deepfake-detection .

docker run -p 8000:8000 deepfake-detection

Access the app at http://localhost:8000.

3. Manual Setup

Follow the YouTube Installation Playlist for step-by-step setup instructions.

🖥️ Usage

Navigate to the Django Application directory.

Start the Django server:python manage.py runserver

Open your browser and visit http://localhost:8000.

Upload a video to detect if it’s a deepfake.

📚 Documentation

Project Documentation

Medium Article for an in-depth overview

🤝 Contribute

We welcome contributions to improve the project! Suggested enhancements include:

Deploying to free cloud platforms.

Creating an open-source API for detection.

Supporting batch processing of entire videos.

Optimizing code Wfor faster execution.

Completed Improvements:

✅ Dockerized the application.

✅ Enabled compatibility with non-CUDA systems (e.g., AMD GPUs, CPUs).

To contribute:

Fork the repository.

Create a feature branch.

Submit a pull request.

📜 License

This project is licensed under the GPLv3 License.

⭐ Support

If you find this project valuable, please give it a star on GitHub to show your support!