Deepfake Detection System | AI-Powered Video Authentication

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📌 Project Overview

Deepfake Detection System is an AI-powered forensic authentication solution that detects manipulated facial videos and images with 93.58% accuracy. Using advanced deep learning techniques (ResNext CNN + LSTM RNN), it identifies media tampering through temporal and spatial feature extraction.

Key Highlights:

• ✅ High Accuracy: 93.58% detection on 100 frames per video
• ✅ Production-Ready: Django web interface + Docker containerization
• ✅ Transfer Learning: Pre-trained ResNext backbone for efficient training
• ✅ Temporal Analysis: LSTM layer captures video sequence patterns
• ✅ Scalable Architecture: Supports batch processing and real-time predictions

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🎯 Why This Matters

Deepfakes pose critical threats to:

• Media Authenticity: Verify video authenticity in journalism & broadcasting
• Security & Fraud Detection: Combat synthetic identity theft & voice spoofing
• Digital Forensics: Support legal investigations & forensic analysis
• Social Platform Safety: Content moderation at scale

This project demonstrates production-grade deep learning for media authentication.

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📸 Screenshots & Demo

System Workflow

Live Detection Demo

Watch Full YouTube Demo | View Step-by-Step Setup Playlist

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✨ Key Features

• Dual-Stage Detection Pipeline

  • ResNext CNN: Extracts spatial features from video frames
  • LSTM RNN: Learns temporal patterns across frame sequences

• Transfer Learning Architecture

  • Pre-trained ResNext backbone reduces training time
  • Fine-tuned for deepfake classification

• Web Interface (Django)

  • Video upload & analysis
  • Real-time prediction results
  • Confidence scores & visualization

• Docker Support

  • One-click deployment
  • No dependency conflicts
  • Tested on CPU & GPU systems

• Flexible Frame Processing

  • 10, 20, 40, 60, 80, 100 frames per video
  • Accuracy improves with more frames
  • Trade-off between speed & precision

• Cross-Platform Compatibility

  • CPU inference (standard systems)
  • GPU acceleration (CUDA)
  • AMD GPU support

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📊 Model Performance

Model	Training Videos	Frames/Video	Accuracy	Detection Speed
Base Model	6,000	10	84.21%	⚡⚡⚡
Enhanced	6,000	20	87.79%	⚡⚡
Standard	6,000	40	89.35%	⚡⚡
Optimized	6,000	60	90.59%	⚡
Advanced	6,000	80	91.50%	⚡
Production Grade	6,000	100	93.59%	Standard

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🛠️ Tech Stack

Deep Learning & Computer Vision

• PyTorch - Deep learning framework
• ResNext-50 - Convolutional neural network (pre-trained ImageNet)
• LSTM - Long Short-Term Memory for sequence learning
• OpenCV - Video processing & frame extraction
• Scikit-learn - Model evaluation & metrics

Web Framework

• Django - Backend web server
• Django REST Framework - API endpoints
• HTML/CSS/JavaScript - Frontend interface

DevOps & Deployment

• Docker - Container orchestration
• CUDA/cuDNN - GPU acceleration (optional)
• pip - Python package management

Data & Preprocessing

• NumPy - Array operations
• Pandas - Data handling
• Pillow (PIL) - Image processing

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🎮 What You Can Do

• Upload Videos - Submit any video file (MP4, AVI, MOV)
• Get Predictions - Receive real/fake classification with confidence score
• View Analysis - Visualize detected manipulations frame-by-frame
• Download Results - Export detection reports
• Batch Processing - Analyze multiple videos simultaneously
• Train Custom Models - Retrain on custom datasets

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🔧 Installation & Setup

Prerequisites

✓ Python 3.8+
✓ pip (Python package manager)
✓ 4GB+ RAM
✓ Git
✓ (Optional) CUDA 11.8+ for GPU acceleration

Clone Repository

git clone https://github.com/ManamoyB/Deepfake-Detection.git
cd Deepfake-Detection

Install Dependencies

pip install -r requirements.txt

Run Django Application (Local)

cd "Django Application"
python manage.py runserver

Visit http://localhost:8000 in your browser.

Run with Docker (Recommended)

cd "Django Application"
docker build -t deepfake-detector .
docker run -p 8000:8000 deepfake-detector

Train Custom Model

cd "Model Creation"
jupyter notebook
# Open and run the training notebooks

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📂 Project Structure

Deepfake-Detection/
├── Django Application/          # Web interface for predictions
│   ├── manage.py               # Django project manager
│   ├── Dockerfile              # Container configuration
│   ├── requirements.txt         # Python dependencies
│   └── templates/              # HTML templates
│
├── Model Creation/              # Training & development notebooks
│   ├── 1_Data_Preparation.ipynb
│   ├── 2_ResNext_Feature_Extraction.ipynb
│   ├── 3_LSTM_Training.ipynb
│   ├── 4_Model_Evaluation.ipynb
│   └── trained_models/         # Pre-trained model weights
│
├── Documentation/               # Project documentation
│   ├── Architecture.md
│   ├── Dataset_Information.md
│   ├── Installation_Guide.md
│   └── API_Reference.md
│
├── github_assets/              # Demo images & GIFs
├── README.md                   # This file
└── LICENSE                     # GPL v3.0 License

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🎓 Development Process

Phase 1: Research & Planning

• Analyzed deepfake detection literature
• Identified ResNext + LSTM as optimal architecture
• Designed two-stage detection pipeline

Phase 2: Data Preparation

• Sourced 6,000+ real & fake videos
• Extracted frames with temporal spacing
• Normalized & augmented training data

Phase 3: Model Development

• Transfer learning with pre-trained ResNext
• LSTM tuning for sequence classification
• Hyperparameter optimization for accuracy

Phase 4: Testing & Validation

• Cross-validated on hold-out test set
• Analyzed failure cases & edge scenarios
• Benchmarked inference speed

Phase 5: Deployment

• Containerized with Docker
• Created Django web interface
• Tested on CPU & GPU systems

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🧠 Architecture Explanation

Video Input
    ↓
[Frame Extraction]
    ↓
[ResNext CNN] ← Pre-trained on ImageNet
    ↓
[Feature Vectors] (2048-D per frame)
    ↓
[LSTM Layer] ← Learns temporal patterns
    ↓
[Classifier]
    ↓
Real/Fake Prediction + Confidence Score

Why This Approach?

1. ResNext: Powerful spatial feature extractor (proven on ImageNet)
2. LSTM: Captures temporal dependencies across frames (deepfakes have telltale patterns)
3. Transfer Learning: Reduces training data needs & computational cost
4. Ensemble Effect: Combining CNN + RNN catches both spatial & temporal anomalies

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📈 Key Learnings

• Transfer Learning Impact: Pre-trained ResNext reduced training time by 60%
• Frame Count Trade-off: More frames = higher accuracy but slower inference
• LSTM Effectiveness: Sequential modeling improved accuracy by 8-10%
• Docker Benefits: Eliminated dependency hell, streamlined deployment
• Data Augmentation: Critical for robust generalization across video qualities

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🚀 Future Improvements

• Real-Time Video Stream Processing - Support live webcam input
• Attention Mechanisms - Add self-attention for temporal focus
• Ensemble Models - Combine with FaceForensics++ models
• Mobile Deployment - TensorFlow Lite for edge devices
• API Service - REST API for third-party integrations
• Explainability - GradCAM visualization of detection regions
• Multi-Language Support - Localized web interface
• Cloud Deployment - AWS/GCP/Azure integration

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💻 Keyboard Shortcuts (Django Web UI)

Action	Shortcut
Upload File	Ctrl + U
Start Analysis	Enter
View Results	Ctrl + R
Download Report	Ctrl + S
Dark Mode Toggle	Ctrl + T

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📚 Documentation & Resources

• Medium Article - Detailed technical writeup
• YouTube Playlist - Step-by-step setup guide
• YouTube Demo - Live system walkthrough
• FaceForensics++ Dataset - Deepfake research benchmark

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🤝 Contributing

This project welcomes contributions! Potential areas:

• Improving model accuracy on diverse datasets
• Optimizing inference speed
• Creating open-source API
• Adding batch video processing
• Improving UI/UX
• Writing additional tests

How to Contribute

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Commit changes (git commit -m "Add improvement")
4. Push to branch (git push origin feature/improvement)
5. Open a Pull Request

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📋 License

This project is licensed under the GNU General Public License v3.0 - see LICENSE file for details.

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

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📞 Contact & Support

Author: Manamoy B

Connect With Me:

• LinkedIn: Manamoy's Profile
• GitHub: @ManamoyB
• Portfolio: [Your Portfolio Website]
• Email: [your.email@example.com]

Questions or Issues?

• Open an GitHub Issue
• Check Documentation folder
• Review FAQ below

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⭐ If You Found This Helpful

If this project helped you or you found it useful for learning, please:

• ⭐ Star this repository to increase visibility
• 🍴 Fork if you plan to build on it
• 💬 Share with your network
• 📧 Mention in your portfolio/resume

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👥 Contributors & Acknowledgments

Lead Developer: Manamoy Banerjee

Thank you to the open-source community for PyTorch, Django, and all dependencies!

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📈 Project Stats

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Last Updated: June 2026 | Status: Active Maintenance | Python 3.8+ | PyTorch 1.9+