DeepScan-AI

AI-powered DeepFake Detection using frame-by-frame video analysis

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📜 Table of Contents

📖 Overview
📷 UI Screenshots
✨ Features
🧠 Technology Stack
🧠 How It Works
📂 Project Structure
🚀 Getting Started
🧑‍💻 Author

📖 Overview

DeepScan-AI is a full-stack AI application for detecting deepfakes in videos. It leverages a custom-trained TensorFlow model to analyze individual frames and computes an overall verdict of authenticity.

📸 UI Screenshots

✨ Features

✅ Feature	Description
🎥 Video Upload	Drag & drop or browse to upload video
📸 Frame-by-Frame Scan	Breaks video into frames and runs each through a trained model
🧠 Deep Learning Model	TensorFlow CNN predicts manipulation score per frame
📊 Final Verdict	Displays averaged score with verdict: ✅ Authentic / ⚠️ Deepfake
🌐 Fully Responsive	Tailwind-powered sleek and modern interface
🚀 FastAPI + Express	Lightweight backend infrastructure

🧠 Technology Stack

DeepScan-AI is built using a modular, full-stack architecture that combines modern web technologies with machine learning. Here's a breakdown of the stack:

🌐 Frontend

Technology	Purpose
Next.js	React-based framework for frontend pages
React	Component-driven UI logic
Tailwind CSS	Utility-first CSS for styling
Lucide Icons	Clean, minimal icon set used in UI
Axios	HTTP client for interacting with the API

🖥️ Backend (Upload & Routing)

Technology	Purpose
Node.js	Runtime environment
Express.js	Upload route and form handling
Multer	Middleware for handling file uploads

🧪 AI/ML Inference Engine

Technology	Purpose
Python	Core language for analysis pipeline
FastAPI	Fast and lightweight API server
TensorFlow	ML framework for deepfake detection
Keras	High-level neural networks API (CNN model usage)
OpenCV	Frame extraction and image processing

🧱 Infrastructure

Local Development using localhost:3000 (frontend), 4000 (upload), and 8000 (inference)
Modular Folder Structure: /client, /server, /analyzer for separation of concerns
REST API communication between services

💡 The stack was chosen to maximize performance, maintainability, and ease of integration between modern web and AI tools.

🧠 How It Works

Upload a video from the client.
The Express server handles file saving.
FastAPI backend extracts frames using OpenCV.
Each frame is evaluated using a CNN deepfake model.
The average of frame scores determines the authenticity verdict.
Results are sent back to the frontend for display.

📂 Project Structure

DeepScan-AI/
├── client/               # Next.js frontend
│   └── pages/
│       └── index.tsx     # Main UploadPage
├── server/               # Node.js Express middleware
│   └── index.js
├── analyzer/             # Python FastAPI + TensorFlow backend
│   ├── main.py
│   ├── mesonet.py
│   └── mesonet_model.h5  # Pretrained model
├── README.md
├── .gitignore

🚀 Getting Started

📋 Prerequisites

Before getting started with DeepScan-AI, make sure you have the following installed:

Node.js (v18+)
npm or yarn
Python (3.8+)
pip and virtualenv

🛠️ Installation

Follow these steps to set up the full DeepScan-AI stack on your machine:

🔍 1. Clone the repository

git clone https://github.com/your-username/DeepScan-AI
cd DeepScan-AI

⚙️ 2. Set up the Deepfake Analysis Backend (FastAPI + TensorFlow)

cd analyzer
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install fastapi==0.95.2 uvicorn==0.22.0 numpy==1.24.4 opencv-python==4.8.0.76 tensorflow==2.11.0 python-multipart==0.0.6 pydantic==1.10.13
uvicorn main:app --reload

The backend will start at: http://localhost:8000

🌐 3. Start the Node.js Upload Server

cd ../server
npm install
node index.js

Upload server will run at: http://localhost:4000

🖼️ 4. Start the Next.js Frontend

cd ../client
npm install
npm run dev

Frontend will be live at: http://localhost:3000

Contributing

💬 Join the Discussions: Share your insights, provide feedback, or ask questions.
🐛 Report Issues: Submit bugs found or log feature requests.
💡 Submit Pull Requests: Review open PRs, and submit your own PRs.

🧑‍💻 Author

Built by Alexander Potiagalov

AlexanderPotiagalov/DeepScan-AI