DeepScan Video Analysis Platform

A Laravel-based web application for analyzing videos using deep learning to detect potential deepfake video content.

Features

• Video upload and processing
• Deepfake detection analysis with visual explanations
• Email notifications with results
• User authentication and authorization
• Report generation and management
• Interactive report interface
  • Side-by-side frame comparison
  • Grad-CAM visualization of suspicious regions
  • On-demand video generation
  • Detailed analysis metrics
• Real-time processing status updates

Project Architecture

This repository is part of a larger DeepScan ecosystem consisting of three main components:

1. DeepScan Platform (this repo)

   • Main web application for video upload and analysis
   • User interface and result visualization
   • Video processing and frame extraction
   • Queue management and notifications
   • Interactive report interface featuring:
     • Side-by-side comparison of original and analyzed frames
     • On-demand video generation of analysis results
     • Detailed frame-by-frame analysis visualization
     • Export and sharing capabilities

2. DeepScan API (deepscan-api)

   • REST API for deepfake detection inference

   • Receives extracted frames from the main platform

     • Frame-by-frame deepfake probability scores
     • Grad-CAM visualizations highlighting suspicious regions
     • Explanation data for model decisions

   • Handles model deployment and scaling

3. DeepScan Model (deepscan-model)

   • Deep learning model training code
   • Dataset preparation and preprocessing
   • Model evaluation and validation
   • Model interpretability and visualization tools

Tech Stack

• Backend Framework: Laravel 11
• Frontend Framework: Vue.js 3 with Inertia.js
• CSS Framework: Tailwind CSS
• Database: MySQL/PostgreSQL
• Queue System: Redis with Laravel Horizon
• File Storage: SFTP support via Flysystem
• Video Processing: FFmpeg integration via pbmedia/laravel-ffmpeg

Prerequisites

• PHP 8.2 or higher
• Composer
• Node.js & NPM
• Redis server
• FFmpeg
• MySQL/PostgreSQL

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/deepscan.git
   cd deepscan

2. Install PHP Dependencies:

   composer install

3. Install Javascript Dependencies:

   npm install

4. Create Enviornment File:

   cp .env.example .env

5. Generate Application Key:

   php artisan key:generate

6. Configure your database in .env file:

   DB_CONNECTION=mysql
   DB_HOST=127.0.0.1
   DB_PORT=3306
   DB_DATABASE=your_database
   DB_USERNAME=your_username
   DB_PASSWORD=your_password

7. Run Migrations:

   php artisan migrate

8. Build Frontend Assets:

   npm run build

Running the Application

1. Start the Development Server:

   php artisan serve

2. Start the Frontend Development Server:

   npm run dev

3. Start the Queue Worker:

   php artisan queue:work deepscan_model
   php artisan horizon

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License.

Acknowledgments

• Laravel Framework
• Vue.js
• Tailwind CSS
• FFmpeg