🛡️ Luminark

AI-Powered Deepfake Video Detection

Detect manipulated videos with 94%+ accuracy using an ensemble of 6 neural network models

Live Demo • Features • Architecture • Installation • API Reference

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✨ Features

Feature	Description
🎯 Multi-Model Ensemble	6 specialized AI models voting on authenticity
🎥 Real-Time Analysis	Process videos up to 500MB with live progress
🔬 Explainable AI	Grad-CAM visualizations and per-model contributions
🌓 Modern UI	Glassmorphism design with light/dark themes
🔒 Privacy-First	Videos deleted immediately after analysis
⚡ Fast Inference	GPU-accelerated or optimized CPU processing

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🧠 Model Architecture

Luminark uses an ensemble fusion approach with 6 specialized detection models:

                    ┌─────────────────┐
                    │   Input Video   │
                    └────────┬────────┘
                             │
         ┌───────────────────┼───────────────────┐
         │                   │                   │
         ▼                   ▼                   ▼
   ┌──────────┐      ┌──────────────┐     ┌───────────┐
   │ VideoMAE │      │ EfficientNet │     │  WavLM    │
   │ (Video)  │      │  (Spatial)   │     │  (Audio)  │
   └────┬─────┘      └──────┬───────┘     └─────┬─────┘
        │                   │                   │
   ┌────┴─────┐      ┌──────┴───────┐     ┌─────┴─────┐
   │ CNN-LSTM │      │ FFT/DCT      │     │  Lip-Sync │
   │(Temporal)│      │ (Frequency)  │     │ Detector  │
   └────┬─────┘      └──────┬───────┘     └─────┬─────┘
        │                   │                   │
        └───────────────────┼───────────────────┘
                            │
                    ┌───────▼───────┐
                    │ Fusion Layer  │
                    │(Weighted Vote)│
                    └───────┬───────┘
                            │
                    ┌───────▼───────┐
                    │    Verdict    │
                    │  REAL / FAKE  │
                    └───────────────┘

Model	Type	Detection Focus
VideoMAE	Transformer	Spatio-temporal patterns
EfficientNet	CNN	Frame-level artifacts
CNN-LSTM	Hybrid	Motion inconsistencies
FFT/DCT	Frequency	Compression artifacts
WavLM	Audio	Voice synthesis traces
Lip-Sync	Multimodal	Audio-visual mismatch

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🖥️ Demo

Screenshots

Light Theme	Dark Theme

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🚀 Quick Start

Prerequisites

• Python 3.10+
• Node.js 18+
• Docker (recommended)

Installation

# Clone repository
git clone https://github.com/IsVohi/Luminark-DeepFake_Detection.git
cd Luminark-DeepFake_Detection

# Option 1: Docker (Recommended)
docker compose up

# Option 2: Manual Setup
# Backend
pip install -r requirements/base.txt
pip install torch torchvision torchaudio
uvicorn backend.app:app --reload

# Frontend
cd frontend && npm install && npm run dev

Access

• Frontend: http://localhost:5173
• Backend API: http://localhost:8000
• API Docs: http://localhost:8000/docs

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📡 API Reference

Endpoints

Method	Endpoint	Description
GET	/health	Health check
POST	/infer	Analyze video (quick)
POST	/explain	Analyze with XAI details

Example Request

curl -X POST http://localhost:8000/infer \
  -H "X-API-Key: your_api_key" \
  -F "video=@test_video.mp4"

Response

{
  "verdict": "FAKE",
  "confidence": 0.94,
  "scores": {
    "spatial": 0.89,
    "temporal": 0.92,
    "frequency": 0.87,
    "audio": 0.96
  },
  "explanation": "High temporal inconsistency detected..."
}

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📁 Project Structure

luminark/
├── backend/          # FastAPI server
│   ├── app.py        # Main application
│   └── sdk/          # Client SDK
├── core/             # ML pipeline
│   ├── models/       # Neural network definitions
│   ├── xai/          # Explainability (Grad-CAM)
│   └── infer.py      # Inference orchestration
├── frontend/         # React + Vite
│   ├── src/
│   │   ├── pages/    # Landing, Analyze, Docs
│   │   └── components/
│   └── public/
├── models/           # Trained weights (download separately)
├── infra/            # Docker, K8s, AWS configs
└── requirements/     # Python dependencies

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🔧 Configuration

Environment Variables

Variable	Description	Default
MODEL_DEVICE	cpu or cuda	cpu
LUMINARK_API_KEYS	Comma-separated API keys	-
LOG_LEVEL	Logging verbosity	INFO

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📊 Performance

Tested on DFDC and Celeb-DF v2 datasets:

Metric	Score
Accuracy	94.2%
AUC-ROC	0.967
F1 Score	0.938
Inference Time	~3s/video

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🛠️ Tech Stack

Backend: Python, FastAPI, PyTorch, OpenCV, FFmpeg

Frontend: React 18, Vite, Framer Motion, Lucide Icons

ML Models: VideoMAE, EfficientNet, WavLM, CNN-LSTM

DevOps: Docker, Kubernetes, AWS Lambda

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📄 License

This project is licensed under the MIT License - see the LICENSE file.

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👤 Author

Vikas Sharma

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_{Built with ❤️ for a safer digital world}