DeepShield — Multi-Modal AI Deepfake Detection

DeepShield is a comprehensive platform for detecting deepfakes across multiple modalities (Video, Image, and Audio). It uses state-of-the-art AI models to analyze media for facial inconsistencies, pixel-level artifacts, and synthetic voice generation.

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🧠 AI/ML Stack

Python:

• Main programming language used for AI development.
• Provides easy integration with ML libraries.

PyTorch:

• Framework used to build, train, and test deep learning models.
• Provides GPU support for faster computation.

EfficientNetV2 (local details):

• Used for image classification.
• Detects fake images by analyzing:
  • Facial inconsistencies
  • Texture and pixel-level artifacts

MTCNN (Multi-task Cascaded Convolutional Network):

• Used for face detection.
• Extracts faces from images/videos.

Wav2Vec:

• Processes raw audio signals.
• Extracts deep audio features.
• Helps detect AI-generated or cloned voices.

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💻 Full-stack Development

React + Tailwind CSS:

• Builds the user interface.
• Provides a responsive and clean design.

Flask:

• Backend framework.
• Responsible for API requests.
• Handles model execution.
• Manages communication between the frontend and AI.

MongoDB:

• NoSQL database.
• Stores: User uploads, Detection Results, and logs.

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🚀 How to Run Locally

1. Backend (Flask API)

cd backend
python -m venv venv
# Windows: venv\Scripts\activate | Mac/Linux: source venv/bin/activate
pip install -r requirements.txt

# Create a .env file with your MongoDB URI
# (Note: Use your text editor to create this file to avoid Windows PowerShell encoding issues)
# Inside backend/.env, add this line:
# MONGO_URI=mongodb://localhost:27017/deepshield

flask run

Runs on http://localhost:5000

2. Frontend (React UI)

Open a new terminal window:

cd frontend
npm install
npm run dev

Runs on http://localhost:5173