🛡️ Deep Shield

Deep Shield is a comprehensive deepfake detection platform designed for interviewers to verify the authenticity of candidates during video calls. The platform combines real-time deepfake detection with a powerful dashboard for analyzing detection results.

🌟 Features

Real-time Deepfake Detection: Capture and analyze screenshots from Google Meet calls using NVIDIA Hive AI
Chrome Extension: Seamless integration with Google Meet for instant detection
Analytics Dashboard: Comprehensive dashboard with charts, statistics, and detection history
MongoDB Storage: Persistent storage of all detection results for analysis
Modern UI: Beautiful, responsive Next.js dashboard with real-time data visualization

🏗️ Architecture

Deep Shield consists of three main components:

Chrome Extension - Captures screenshots from Google Meet and sends them for analysis
Backend API - Express.js server that processes images using NVIDIA Hive API and stores results in MongoDB
Frontend Dashboard - Next.js application for viewing and analyzing detection results

🛠️ Tech Stack

Backend

Node.js with Express.js
MongoDB with Mongoose
NVIDIA Hive API for deepfake detection
RESTful API architecture

Frontend

Next.js 15 with React 19
TypeScript for type safety
Tailwind CSS for styling
Recharts for data visualization
shadcn/ui components

Chrome Extension

Manifest V3
Service Worker for background processing
Content Scripts for Google Meet integration

📋 Prerequisites

Before you begin, ensure you have the following installed:

Node.js (v18 or higher)
pnpm (or npm/yarn)
MongoDB (local installation or MongoDB Atlas account)
Chrome Browser (for the extension)
NVIDIA API Key (for deepfake detection)

🚀 Getting Started

1. Clone the Repository

git clone https://github.com/fashton28/DeepShield.git
cd DeepShield

2. Backend Setup

Install Dependencies

cd backend
npm install

Configure Environment Variables

Create a .env file in the backend directory:

cp .env.example .env

Edit .env with your configuration:

PORT=3000
MONGODB_URI=mongodb+srv://username:password@cluster.mongodb.net/deepfake-detection
NVIDIA_API_KEY=your_nvidia_api_key_here

Start the Backend Server

npm start
# or for development with auto-reload
npm run dev

The backend will start on http://localhost:3000

3. Frontend Setup

Install Dependencies

cd frontend
pnpm install

Configure Environment Variables

Create a .env.local file in the frontend directory:

NEXT_PUBLIC_API_URL=http://localhost:3000

Start the Frontend Development Server

pnpm dev

The frontend will start on http://localhost:3001 (or the next available port)

4. Chrome Extension Setup

Load the Extension

Open Chrome and navigate to chrome://extensions/
Enable "Developer mode" (toggle in the top right)
Click "Load unpacked"
Select the chrome-extension directory from this project
The extension should now appear in your extensions list

Add Extension Icons (Optional)

The extension requires icon files. Create or add the following to chrome-extension/icons/:

icon16.png (16x16 pixels)
icon48.png (48x48 pixels)
icon128.png (128x128 pixels)

See chrome-extension/icons/README.md for more details.

📖 Usage

Using the Chrome Extension

Navigate to https://meet.google.com/ and join or start a meeting
Click the Deep Shield extension icon in your Chrome toolbar
Click "Capture & Analyze" to capture a screenshot and analyze it
View the results showing:
- Fake Probability percentage
- Confidence score
- Status (Likely Real, Uncertain, or Likely Deepfake)

Using the Dashboard

Open the dashboard at http://localhost:3001/dashboard
View real-time statistics:
- Average Deepfake Percentage
- Total Detections
- Authentic Detections
- Recent Activity (last 24 hours)
Explore charts and visualizations:
- Deepfake Detection Trend (line chart)
- Fake Probability Distribution (bar chart)
Review detection history in the table with:
- Date and time
- Fake probability and confidence scores
- Status indicators

📁 Project Structure

DeepShield/
├── backend/                 # Express.js backend API
│   ├── db/                 # MongoDB models and connection
│   │   ├── connection.js   # Database connection
│   │   └── models.js       # Mongoose schemas
│   ├── routes/             # API routes
│   │   └── deepfake.js     # Deepfake detection endpoints
│   ├── services/           # Business logic
│   │   └── hiveService.js  # NVIDIA Hive API integration
│   ├── server.js           # Express server entry point
│   └── package.json        # Backend dependencies
│
├── frontend/               # Next.js frontend
│   ├── app/               # Next.js app router
│   │   ├── dashboard/     # Dashboard page
│   │   ├── layout.tsx     # Root layout
│   │   └── page.tsx       # Landing page
│   ├── components/        # React components
│   │   ├── ui/           # shadcn/ui components
│   │   ├── header.tsx    # Navigation header
│   │   └── hero-content.tsx # Landing page hero
│   ├── lib/              # Utilities
│   │   └── api.ts        # API client functions
│   └── package.json      # Frontend dependencies
│
├── chrome-extension/      # Chrome extension
│   ├── manifest.json     # Extension manifest
│   ├── background.js     # Service worker
│   ├── content.js        # Content script
│   ├── popup.html        # Extension popup UI
│   ├── popup.js          # Popup logic
│   ├── popup.css         # Popup styles
│   └── icons/            # Extension icons
│
└── README.md             # This file

🔌 API Endpoints

Deepfake Detection

POST `/api/deepfake`

Analyze an image for deepfake detection.

Request:

{
  "image": "<base64_encoded_image>",
  "timestamp": "2024-01-01T12:00:00.000Z"
}

Response:

{
  "success": true,
  "result": {
    "fake_probability": 0.164,
    "confidence": 0.874,
    "bounding_boxes": [...],
    "timestamp": "2024-01-01T12:00:00.000Z"
  }
}

GET `/api/deepfake/results`

Get detection results from the database.

Query Parameters:

limit (optional): Number of results (default: 50)
sort (optional): Sort order - asc or desc (default: desc)
startDate (optional): Filter by start date (ISO string)
endDate (optional): Filter by end date (ISO string)

Response:

{
  "success": true,
  "data": [
    {
      "_id": "...",
      "deepfakePercentage": 0.164,
      "confidence": 0.874,
      "createdAt": "2024-01-01T12:00:00.000Z",
      "updatedAt": "2024-01-01T12:00:00.000Z"
    }
  ],
  "total": 100,
  "limit": 50
}

Health Check

GET `/health`

Check if the server is running.

Response:

{
  "status": "ok",
  "timestamp": "2024-01-01T12:00:00.000Z"
}

🗄️ Database Schema

DeepfakeDetection Collection

{
  _id: ObjectId,
  deepfakePercentage: Number,  // 0-1 (probability of being fake)
  confidence: Number,           // 0-1 (model confidence)
  createdAt: Date,
  updatedAt: Date
}

🔧 Configuration

Backend Configuration

PORT: Server port (default: 3000)
MONGODB_URI: MongoDB connection string
NVIDIA_API_KEY: Your NVIDIA Hive API key

Frontend Configuration

NEXT_PUBLIC_API_URL: Backend API URL (default: http://localhost:3000)

Chrome Extension Configuration

BACKEND_URL: Backend API endpoint (configured in background.js)

🐛 Troubleshooting

Backend Issues

MongoDB Connection Error:

Verify your MONGODB_URI in .env is correct
Ensure MongoDB is running (local) or your Atlas cluster is accessible
Check network connectivity

NVIDIA API Errors:

Verify your NVIDIA_API_KEY is correct and has proper permissions
Check API rate limits
Ensure image size is within limits (< 180KB base64 for direct upload)

Frontend Issues

API Connection Errors:

Verify NEXT_PUBLIC_API_URL matches your backend URL
Ensure the backend server is running
Check CORS settings in the backend

No Data Displayed:

Check browser console for errors
Verify backend API is returning data
Check network tab for failed requests

Chrome Extension Issues

Extension Not Loading:

Ensure all required files are present
Check manifest.json for syntax errors
Verify icons are in the correct location

Screenshot Not Working:

Ensure you're on a Google Meet page
Check browser permissions for the extension
Verify the backend URL is correct in background.js

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

📝 License

This project is licensed under the ISC License.

🙏 Acknowledgments

NVIDIA Hive API for deepfake detection capabilities
Next.js and React communities
shadcn/ui for beautiful UI components

📧 Support

For issues, questions, or contributions, please open an issue on the GitHub repository.

Built with ❤️ for reliable candidate verification

Name		Name	Last commit message	Last commit date
Latest commit History 9 Commits
backend		backend
chrome-extension		chrome-extension
electron		electron
frontend		frontend
.gitignore		.gitignore
README.md		README.md
package.json		package.json

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🛡️ Deep Shield

🌟 Features

🏗️ Architecture

🛠️ Tech Stack

Backend

Frontend

Chrome Extension

📋 Prerequisites

🚀 Getting Started

1. Clone the Repository

2. Backend Setup

Install Dependencies

Configure Environment Variables

Start the Backend Server

3. Frontend Setup

Install Dependencies

Configure Environment Variables

Start the Frontend Development Server

4. Chrome Extension Setup

Load the Extension

Add Extension Icons (Optional)

📖 Usage

Using the Chrome Extension

Using the Dashboard

📁 Project Structure

🔌 API Endpoints

Deepfake Detection

POST /api/deepfake

GET /api/deepfake/results

Health Check

GET /health

🗄️ Database Schema

DeepfakeDetection Collection

🔧 Configuration

Backend Configuration

Frontend Configuration

Chrome Extension Configuration

🐛 Troubleshooting

Backend Issues

Frontend Issues

Chrome Extension Issues

🤝 Contributing

📝 License

🙏 Acknowledgments

📧 Support

About

Resources

Stars

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Packages

Contributors

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POST `/api/deepfake`

GET `/api/deepfake/results`

GET `/health`