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Paradox: Recursive Visual Entropy Key Derivation Engine (RVE-KDE)

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Python Version License: MIT Status: Research Prototype DOI


1. Project Overview & Motivation

Paradox is an experimental cryptographic key derivation engine that maps high-dimensional visual media (images) to symmetric keys deterministically.

Why Visual Entropy?

Traditional key derivation functions (like PBKDF2 or bcrypt) process low-entropy, linear inputs (passwords). In contrast, images represent high-dimensional physical entropy source matrices. Paradox leverages this visual media matrix to establish a deterministic "visual factor" for key agreement, file archiving, and cover-medium key setup.


2. Core Architecture

The pipeline processes input visual data and nonces through seven discrete layers:

  1. Initial Seed Generator: Formulates Seed0 = SHA3-512(ImageHash + Nonce + Timestamp + Version).
  2. Recursive Walker: Slices coordinates modulo dimensions and traverses local adjacent neighbors.
  3. Luminance & Contrast Extractor: Extracts color states, perceived brightness, and local grid standard deviations.
  4. Hash Chain Evolved State: Formulates sequential hash linkages of coordinates, pixel bytes, and neighbor values.
  5. Multi-Layer Recursion Manager: Runs dependent walks in sequence (Layer $n$ seed depends on Layer $n-1$'s final state) to diffuse spatial dependencies.
  6. Entropy Pool Collector: Aggregates SHA3-256 slices from each step into a master entropy pool.
  7. KDF Compressor: Squeezes the pool into 128/256/512-bit keys using HKDF-SHA256 or BLAKE3-KDF.

3. Installation & Developer Setup

Clone the repository, install Maturin/build dependencies, and build the Rust native extension in release mode:

python3 -m venv .venv
source .venv/bin/activate
pip install maturin patchelf
export PYO3_USE_ABI3_FORWARD_COMPATIBILITY=1
maturin develop --release

4. Quick Start & Examples

Text Encryption

import paradox

img = paradox.useImage("sample.png")

# Encrypts message and returns metadata envelope
encrypted = paradox.encrypt_text("Confidential message", image=img, security_level="low")

# Decrypts payload deterministically
decrypted = paradox.decrypt_text(encrypted, image=img)
print(decrypted) # "Confidential message"

File Encryption

import paradox

img = paradox.useImage("sample.png")
paradox.encrypt_file("doc.pdf", "doc.pdf.enc", image=img, security_level="low")
paradox.decrypt_file("doc.pdf.enc", "doc_dec.pdf", image=img)

5. API Reference

Category Functions
Image Loading useImage(path), getRandomImage()
Key Derivation generate_key(image, len, level, kdf), generate_key128(), generate_key256(), generate_key512()
Encryption/Decryption encrypt_text(), decrypt_text(), encrypt_file(), decrypt_file()
Diagnostics visualize_walk(), analyze_image()

6. Experimental Validation Results

  • Entropy Density: Derived keys exhibit a Shannon entropy of 7.99946 bits/byte (squeezed via HKDF-SHA256).
  • Chi-Square Goodness-of-Fit: Passed ($p = 0.75 > 0.01$), confirming uniform byte distributions.
  • Avalanche Effect: 50.09% mean bit difference following a 1-bit input offset.
  • Collision Rate: 0.00% collision count over 3,000 keys.

For detailed plots and analysis, see validation_report.md and comparison_report.md.


7. Comparative Benchmark Metrics

Symmetric key (256-bit) latency and memory footprints compared to traditional standards:

Metric Category Paradox v1 (Python) Paradox v2 (Rust) PBKDF2-SHA256 HKDF-SHA256 Argon2id (ID) BLAKE3-KDF
Latency LOW 110.35 ms 7.64 ms 0.54 ms 0.26 ms 6.37 ms 0.04 ms
Latency HIGH 37,303.10 ms 1,866.31 ms 18.45 ms 0.17 ms 200.25 ms 0.06 ms
Peak Mem HIGH 104.62 MB 104.62 MB <0.01 MB <0.01 MB 256.00 MB <0.20 MB
Uniqueness (10k) 100% Unique 100% Unique 100% Unique 100% Unique 100% Unique 100% Unique
Vulnerabilities Nonce-reuse risk Nonce-reuse risk GPU cracking Salt reuse Param tuning Context collision

8. Citation & Acknowledgements

If you use Paradox in your cryptographic research or academic publications, please cite it using:

@software{paradox_kdf2026,
  author = {Chirag Ferwani},
  title = {Paradox: Recursive Visual Entropy Key Derivation Engine},
  url = {https://github.com/chiragferwani/paradox},
  version = {1.0.2},
  year = {2026},
  doi = {10.5281/zenodo.20811708}
}

Acknowledgements

  • Matplotlib and NumPy contributors for diagnostic utilities.
  • The Google DeepMind pair-programming agent workspace.
  • Established standard KDF authors (Argon2, PBKDF2, BLAKE3).

9. License

Licensed under the MIT License - see LICENSE for details.


[!WARNING] Paradox is an experimental research-oriented key derivation framework and should not be considered a replacement for established cryptographic standards such as Argon2, PBKDF2, HKDF, or BLAKE3.

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