Feb 16, 2026
Chen Hyperchaotic-AES Medical Image Encryption Protocol
- Mutallip Sattar1
- 1Xinjiang University of Finance and Economics
Protocol Citation: Mutallip Sattar 2026. Chen Hyperchaotic-AES Medical Image Encryption Protocol. protocols.io https://dx.doi.org/10.17504/protocols.io.5qpvo12pbg4o/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: February 14, 2026
Last Modified: February 16, 2026
Protocol Integer ID: 243316
Keywords: aes medical image encryption protocol, aes medical image encryption protocol this protocol, layer encryption scheme for medical image, layer encryption scheme, medical image, complete encryption, chen hyperchaotic, decryption workflow, protocol this protocol, protocol, arnold cat map permutation, combining arnold cat map permutation, chen, chen hyperchaotic system diffusion
Abstract
This protocol describes a three-layer encryption scheme for medical images combining Arnold cat map permutation, Chen hyperchaotic system diffusion, and AES-256 block cipher. The implementation includes synthetic chest X-ray phantom generation, complete encryption/decryption workflows, and validation procedures. All source code is provided as an attachment.
Attachments
Troubleshooting
Step 1: Environment Setup and Dependencies
Install required Python packages:
pip install numpy==1.24.3 opencv-python==4.8.1 pycryptodome==3.19.0
System Requirements:
- Python 3.9, 3.10, or 3.11 (3.10 recommended)
- 4GB RAM minimum (8GB recommended)
- No GPU required (CPU-only implementation)
Note: This protocol uses pure NumPy implementations to avoid SciPy version conflicts.
Step 2: Generate Synthetic Medical Image
Execute the test image generator:
python generate_medical_sample.py
This creates a 512×512 pixel chest X-ray phantom containing:
- Thoracic cage (elliptical boundary)
- Spinal column and rib structures
- Heart shadow (left-central high-density region)
- Pulmonary textures (simulated vasculature)
- Simulated pulmonary nodule (right upper zone)
Output file: realistic_chest_xray.png
Expected runtime: <5 seconds
Step 3: Three-Layer Encryption Process
Run the encryption protocol:
python encrypt_medical.py
This implements the three-layer security scheme:
Layer 1 - Arnold Cat Map Permutation:
Scrambles pixel positions using modular matrix transformation
Formula: [x'; y'] = [1 a; b ab+1] × [x; y] mod N
Layer 2 - Chen Hyperchaotic Diffusion:
Generates chaotic sequence via 4D Chen system (RK4 solver)
XOR operation: Ciphertext = Plaintext ⊕ Chaotic_sequence
Layer 3 - AES-256 Block Encryption:
Standard FIPS-197 implementation (ECB mode, PKCS7 padding)
Outputs:
- encrypted_output.png (ciphertext, appears as random noise)
- encryption_params.npz (key material, KEEP SECURE)
Expected runtime: 2-4 minutes (Intel i5 processor)
Step 4: Decryption and Validation
Restore the original image:
python decrypt_medical.py
Requirements:
- encrypted_output.png (from Step 3)
- encryption_params.npz (key file from Step 3)
Inverse operations:
1. AES-256 decryption (PyCryptodome)
2. Chen hyperchaotic inverse (XOR is self-inverse)
3. Arnold inverse permutation (inverse matrix)
Validation:
The script automatically calculates pixel difference between original and decrypted images.
Expected result: Total difference = 0 (perfect reconstruction)
Output: decrypted_final.png
Expected runtime: 2-4 minutes