SteganoScript

A TypeScript-based steganography application that enables message hiding within digital images. Developed as part of Digital Image Processing coursework, this CLI tool implements advanced pixel manipulation techniques to embed and extract text messages while maintaining visual integrity.

Contents

• 🛠️ Technologies
• 🚀 Getting Started
  • Prerequisites
  • Installation & Setup
  • CLI Usage
    • Encoding Mode
    • Decoding Mode
    • Important Notes
• 📊 Methodology
  • Encoding Process
  • Decoding Process
• 📂 Project Structure
• ⚙️ Features
• 🔧 Usage Examples
  • Basic Encoding
  • Basic Decoding
  • Utility Commands
• 📷 Demonstration
• 📚 Academic Article
• 📝 License
• 👨‍💻 Authors

🛠️ Technologies

Built with:

• Node.js
• TypeScript
• Canvas
  
  

🚀 Getting Started

Prerequisites

Before running the application, ensure the following tools are installed:

• Node.js
• Git

Installation & Setup

# Clone the repository
$ git clone https://github.com/RiadOliveira/SteganoScript.git

# Navigate to project directory
$ cd SteganoScript

# Install dependencies
$ npm install

CLI Usage

SteganoScript offers a powerful CLI with comprehensive options for image steganography operations:

Encoding Mode

Embed secret messages into images with these options:

Option	Alias	Required	Description
--mode encoding	-m	Yes	Set operation mode to message embedding
--imagePath <path>	-i	Yes	Path to carrier image (JPG/PNG)
--pathMessage <path>	-p	Conditional*	Path to .txt file containing secret message
--directMessage "text"	-d	Conditional*	Direct text input of secret message
--outputFileName <name>	-o	No	Custom output filename (default: "outputFile")

*Either pathMessage or directMessage required for encoding.

Decoding Mode

Extract hidden messages from images:

Option	Alias	Required	Description
--mode decoding	-m	Yes	Set operation mode to message extraction
--imagePath <path>	-i	Yes	Path to encoded image file
--outputFileName <name>	-o	No	Custom output filename for extracted message

Important Notes

• Output files are saved in the /outputs directory by default.
• Supported image formats: JPG, PNG.
• Maximum message capacity depends on image dimensions.
• During encoding, original image dimensions are preserved.
  
  

📊 Methodology

Encoding Process

1. Pixel Hashing: Each pixel is converted to a character code using the weighted formula: (1 × R + 5 × G + 20 × B) % 256.
2. RGB Adjustment: If needed, RGB values are incrementally adjusted to match target characters.
3. Positioning: Starting pixel is calculated based on image dimensions using Euler's constant.
4. Distribution: Message is spread across image using a cyclic jump pattern (2,4,6,8 pixels).
5. Validation: Strict bounds checking ensures all RGB values remain within 0-255 range.

Decoding Process

1. Initial Position: Same Euler-based calculation locates first encoded pixel.
2. Pattern Recognition: Cyclic jump sequence reconstructs original message distribution.
3. Character Extraction: Weighted hashing converts RGB values back to ASCII characters.
4. Message Reconstruction: Header metadata ensures accurate message length recovery.
   
   

📂 Project Structure

SteganoScript/
├── outputs/          # Generated encoded images and decoded messages
├── temp/             # Temporary files for processing
├── src/
│   ├── constants/    # RGB weights and operational constants
│   ├── decoding/     # Message extraction logic
│   ├── encoding/     # Message embedding logic
│   ├── error/        # Custom error handling
│   ├── types/        # TypeScript interfaces
│   ├── utils/        # Core operations and validations
│   └── main.ts       # CLI entry point
├── .eslintrc         # Code style configuration
├── package.json      # Project dependencies
└── tsconfig.json     # TypeScript configuration

⚙️ Features

• Imperceptible Alterations - Minimal RGB adjustments ensure visual integrity of carrier images.
• Weighted Hashing Algorithm - Proprietary RGB weighting system (1:5:20 ratio) for character encoding.
• Adaptive Pixel Selection - Intelligent pixel distribution using Euler-based positioning and cyclic jumps.
• CLI Interface - Simple command-line operation for easy integration into workflows.
• Dual Input Modes - Accept messages via text files or direct CLI input.
• Input Validation - Comprehensive checks for file paths and required arguments.
• Help System - Built-in documentation with --help flag.
• Error Handling - Clear error messages for invalid operations.
  
  

🔧 Usage Examples

Basic Encoding

# From text file
$ npm run dev -- -m=encoding -i=./temp/lena.jpg -p=./temp/message.txt

# Direct message input
$ npm run dev -- -m encoding -i=./temp/lena.jpg -d="Super Secret Message" -o=secret_lena

Basic Decoding

$ npm run dev -- -m=decoding -i=./outputs/secret_lena.png -o=extracted_message

Utility Commands

# Show help
$ npm run dev -- --help

# Check version
$ npm run dev -- --version

📷 Demonstration

$ npm run dev -- -m encoding -i=./temp/lena.jpg -d="Super Ultra Secret Message"

Original	Encoded

📚 Academic Article

This project was developed for our Digital Image Processing course and includes a technical paper explaining the methodology. If you're curious about the implementation details: 📖 Read the full article (PDF, 17 pages - in Brazilian Portuguese).

📝 License

This project is MIT Licensed. See LICENSE file for more details.

👨‍💻 Authors

Ríad Oliveira

Lucas Paulino