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Image Encryption Algorithm Based on an Improved ML Neuron Model and DNA Dynamic Coding.

Yan Cao¹, Peng Shi¹, Kaijun Wu¹

¹School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

Computational Intelligence and Neuroscience

|May 24, 2022

Summary

This summary is machine-generated.

This study introduces a novel image encryption algorithm using ML neuron models and DNA dynamic coding. The enhanced method offers a larger key space and superior security against attacks compared to existing low-dimensional chaotic systems.

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Area of Science:

Computer Science
Cryptography
Image Processing

Background:

Low-dimensional chaotic encryption algorithms suffer from small key spaces and low security.
Existing methods lack robustness against sophisticated cryptanalytic attacks.

Purpose of the Study:

To propose a novel image encryption algorithm addressing the limitations of low-dimensional chaotic systems.
To enhance image security using ML neuron models and dynamic DNA coding.

Main Methods:

Block processing of R, G, B image components.
DNA encoding, operation, and decoding dynamically controlled by ML neuron chaotic sequences.
Determinant permutation using logistic map-generated chaotic sequences.

Main Results:

The proposed algorithm achieves a significantly larger key space.
Demonstrated effectiveness in resisting statistical and differential attacks.
Enhanced security and complexity compared to existing methods.

Conclusions:

The ML neuron model and DNA dynamic coding offer a robust solution for secure image encryption.
The algorithm provides a substantial improvement in security and key space for chaotic encryption systems.