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Steganography toolkit for image processing and secure data hiding with modular algorithm injection for research and experimentation.

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StegLab

A steganography toolkit for image processing and secure data hiding with modular algorithm injection, designed for research and practical experimentation.


🚀 Features

  • Image Steganography: Hide and extract data within images securely.
  • Algorithm Injection: Plug-and-play architecture for injecting custom algorithms (DCT, DWT, and beyond).
  • Image Processing: Includes pre-processing capabilities for optimized data hiding.
  • Secure Data Hiding: Supports layered steganography–cryptography workflows.
  • Research-Friendly: Designed for testing, benchmarking, and extending new methods easily.

🛠️ Planned Functionalities

  • Video steganography extension.
  • Multiple payload injection and extraction workflows.
  • Attack simulations (LSB, compression, cropping).
  • Steganalysis testbed integration.
  • Batch processing for dataset-level testing.

📦 Installation

1️⃣ Clone the repository:

git clone https://github.com/thecloudwalkerx/StegLab.git

2️⃣ Navigate to the project directory:

cd StegLab

3️⃣ Install dependencies (if applicable):

pip install -r requirements.txt

⚠️ This project will provide pre-built Windows executables after stable releases.


🖥️ Usage

  • Inject an Algorithm: Place your custom algorithm in the algorithms/ directory following the interface provided.
  • Run StegLab: Execute the GUI executable or run the main script:
python main.py
  • Select Image: Load your target image.
  • Apply Algorithm: Choose your injection algorithm for embedding or extraction.
  • Export Result: Save your processed image with embedded data securely.

🧩 Modular design enables adding, testing, and replacing algorithms without modifying the core structure.


🤝 Contributing

Contributions, algorithm injections, and steganalysis plugins are welcome! Please open an issue first to discuss the feature you wish to contribute.


📜 License

This project will use the MIT License (or your preferred license here).


🙌 Acknowledgements

  • This project serves as a baseline for advanced steganography research and implementation.
  • Inspired by modern steganographic and steganalysis research papers for practical academic workflow enhancement.

🔗 Connect


Happy Hiding! 🕵️‍♂️🖼️🔐

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Steganography toolkit for image processing and secure data hiding with modular algorithm injection for research and experimentation.

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