MauiMapMon

1st gen) a chaos-based PRNG using the logistic map to generate cryptographic keys, blending Hawaiian vibes, reggae rhythms, and secure randomness for key generation.

2nd GEN:

MauiMapMon

MauiMapMon is a Python-based chaotic image generator that leverages chaotic maps (Logistic, Tent, and Henon) to produce random images and derive cryptographic keys. It includes NIST randomness tests to validate sequence randomness and supports both a graphical user interface (GUI) and a command-line interface (CLI) for flexibility across environments.

Features

• Chaotic Maps: Generate sequences using Logistic, Tent, or Henon maps.
• Image Generation: Create visually unique images from normalized chaotic sequences.
• NIST Randomness Tests: Validate randomness with Frequency and Runs tests.
• Cryptographic Key Derivation: Derive secure keys using PBKDF2-HMAC-SHA256.
• GUI and CLI Support: Use a Tkinter-based GUI or CLI for headless environments.
• Security Enhancements:
  • Secure random seed initialization with os.urandom.
  • Input sanitization to prevent invalid or malicious inputs.
  • Enhanced key stretching with high PBKDF2 iteration counts.
• Image Saving: Save generated images as PNG files.
• Visualization: Display chaotic patterns using Matplotlib.

Requirements

• Python 3.8+
• Required packages:
  • numpy
  • scipy
  • cryptography
  • pillow
  • matplotlib
  • tkinter (optional for GUI, included in standard Python library)

Install dependencies using:

pip install numpy scipy cryptography pillow matplotlib

Usage

GUI Mode

Run the script in a graphical environment:

python mauimapmon.py

• Enter initial condition (x0), parameter (r, mu, or a), and select a map type.
• Click "Generate Image" to create and display the image.
• Click "Save Image" to save as a PNG file.

CLI Mode

Run in a terminal or non-graphical environment:

python mauimapmon.py --cli

• Follow prompts to input x0, parameter, map type, and output filename.
• The image is generated and saved as specified.

Example

Generate an image using the Logistic map with x0=0.5 and r=3.9:

• GUI: Enter 0.5 for x0, 3.9 for parameter, select "Logistic," and generate.
• CLI:

  Enter initial condition (x0, e.g., 0.5): 0.5
  Enter parameter (r, mu, or a, e.g., 3.9): 3.9
  Enter map type (logistic, tent, henon): logistic
  Enter output filename (e.g., output.png): chaotic.png

Output includes NIST test results and a derived key in the console.

Security Notes

• Input Validation: Parameters and filenames are sanitized to prevent exploits.
• Randomness: Sequences are seeded with cryptographically secure randomness.
• Key Derivation: Uses 500,000 PBKDF2 iterations for robust key security.

Limitations

• GUI requires a display environment (e.g., X11, Wayland). Use CLI for headless systems.
• Large images may require significant computation time due to chaotic iterations.
• Henon map parameters are constrained to ensure stability (a ≤ 1.4).

License

MIT License. See LICENSE for details.

Contributing

Contributions are welcome! Please submit issues or pull requests on GitHub.

##acknowledgements: Grok 3 by xAI, Google Colab, Microsoft Copilot were used to generate and modify this python script for the purposes of learning and teaching others how to code and understand code.

Chaotic Maps Explained MauiMapMon uses three chaotic maps to generate sequences. Each map exhibits chaotic behavior, producing unpredictable yet deterministic sequences ideal for randomness applications.

Logistic Map: Definition: Defined by the equation x_{n+1} = r * x_n * (1 - x_n), where x_n is the current state (between 0 and 1) and r is a control parameter. Behavior: For r between 3.57 and 4, the map produces chaotic sequences sensitive to initial conditions (x0). Small changes in x0 or r lead to vastly different outputs. Use Case: Generates pseudo-random sequences for image pixel values due to its simplicity and chaotic properties.

Tent Map: Definition: Defined as x_{n+1} = mu * x_n if x_n < 0.5, else x_{n+1} = mu * (1 - x_n), where x_n is the state (between 0 and 1) and mu is a parameter (typically 0 to 2). Behavior: Resembles a tent shape, producing chaotic sequences for mu close to 2. It’s highly sensitive to initial conditions, similar to the Logistic map. Use Case: Provides an alternative chaotic sequence with uniform distribution properties, suitable for cryptographic applications.

Henon Map: Definition: A two-dimensional map defined by x_{n+1} = 1 - a * x_n^2 + y_n and y_{n+1} = b * x_n, where a and b are parameters (typically a=1.4, b=0.3). Behavior: Produces chaotic sequences in a two-dimensional phase space, creating complex patterns. It’s more computationally intensive but offers richer dynamics. Use Case: Generates intricate sequences for applications requiring higher-dimensional chaos, enhancing randomness in images.