🎭 Deepfake Detection System

A deep learning-based web application to automatically detect whether a given video is real or AI-generated (deepfake). This system uses ResNeXt CNN for spatial feature extraction and LSTM RNN for temporal sequence analysis, integrated with a Django web app for easy usage.

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📽️ Live Demo

🚀 Upload a video and get results instantly:

• Classification: REAL / FAKE
• Model Confidence: ✅ Confidence Score
• Rendered Output: Overlay result on video in browser

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📌 Features

• 📹 Upload video and detect deepfakes
• 🧠 Uses pre-trained ResNeXt50_32x4d + LSTM
• 🔐 Video is encrypted during processing
• 🧪 Tested on multiple datasets: FaceForensics++, DFDC, Celeb-DF
• 📊 Achieves up to 97.7% accuracy on benchmark datasets

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📂 Project Structure

Deepfake-Detection/
│
├── models/              # Trained model files (upload yours here)
├── static/              # CSS, JS, media assets
├── templates/           # Django HTML templates (UI)
├── video_processing/    # Preprocessing logic
├── deepfake_detector/   # Django app
├── manage.py            # Django project manager
├── requirements.txt     # Python dependencies
└── README.md            # This file

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🧠 Model Architecture

• ✅ ResNeXt-50 CNN extracts 2048-dim feature vectors per frame
• ⏱ LSTM RNN analyzes sequential frame data (temporal inconsistencies)
• 🧮 Softmax gives prediction + confidence
• 🔁 Trained on 6000 videos (50/50 real-fake split)

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💾 Installation & Setup Guide

1. ⚙️ Clone the Repository

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

2. 🐍 Create a Virtual Environment (Recommended)

python -m venv venv
source venv/bin/activate      # On Windows: venv\Scripts\activate

3. 📦 Install Dependencies

pip install -r requirements.txt

4. 📁 Add Trained Models

Place all your trained .pt model files inside the models/ directory.
If you don’t have them yet, train your model first using your training pipeline.

5. 🔄 Migrate Database

python manage.py migrate

6. 🚀 Run the Server

python manage.py runserver 0.0.0.0:8000

Then visit: http://localhost:8000

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🔒 Security & Privacy

• 🔐 All uploaded videos are encrypted before processing
• 🕓 Stored videos are automatically deleted after 30 minutes
• ✅ Uses SSL certificates for secure connections (production)

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📊 Datasets Used

Dataset	Description
FaceForensics++	Face manipulation benchmark dataset
Deepfake Detection Challenge (DFDC)	From Kaggle's DFDC challenge
Celeb-DF	Celebrity-based real & fake videos
YouTube Real Data	Additional real-world test scenarios

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⚙️ Technologies Used

Stack	Tools
Language	Python 3
Deep Learning	PyTorch, ResNeXt, LSTM
Video Processing	OpenCV, face_recognition
Web Framework	Django
Deployment	Google Cloud Platform (GCP)
UI/UX	HTML, CSS, JavaScript
Security	Symmetric encryption, SSL

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🧪 Test Cases (Examples)

Action	Expected Result
Upload video with no faces	Error: No faces detected
Upload valid real video	Output: "REAL"
Upload deepfake video	Output: "FAKE"
Upload video > 100MB	Error: Max size exceeded
Upload .docx file	Error: Invalid file type

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📈 Results

Dataset	Accuracy (%)	Sequence Length
FaceForensics++	97.76%	100 frames
Celeb-DF + FF++	93.97%	100 frames
Mixed Dataset	89.34%	40 frames

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🚧 Future Scope

• Detect audio and full-body deepfakes
• Convert app to a mobile-compatible version
• Release a browser plugin for video content analysis
• Real-time detection using ONNX / TensorFlow Lite

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🧑‍💻 Maintainer

Made with ❤️ by Karthik U Rao
📬 GitHub

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📄 License

This project is licensed under the MIT License. See the LICENSE file for details.