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Image Encryption Based on Hopfield Neural Network and Bidirectional Flipping.

Haitao Zhang¹, Shuangqi Yang¹

¹School of Software, Liaoning Technical University, Huludao 125105, China.

Computational Intelligence and Neuroscience

|February 21, 2022

Summary

This study introduces a novel image encryption method using a Hopfield neural network and bidirectional flipping. The technique enhances security by linking the encryption key to the plaintext and employing complex chaotic sequences for robust data protection.

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Area of Science:

Cryptography
Computer Science
Artificial Intelligence

Background:

Traditional encryption systems often lack plaintext-dependent keys and utilize single chaotic sequences, posing security vulnerabilities.
Existing methods struggle with key generation and the complexity of chaotic system parameters.

Purpose of the Study:

To propose a novel image encryption method addressing key independence and single chaotic sequence limitations.
To enhance image security through a combination of neural networks and chaotic dynamics.

Main Methods:

Image segmentation and block scrambling followed by bidirectional flipping for permutation.
Hash algorithm utilization for generating chaotic system parameters and pseudo-random sequences.
Hopfield neural network optimization for diffusion matrix generation and diffusion transformation.

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Main Results:

The proposed algorithm demonstrates high sensitivity to plaintext, ensuring data integrity.
It exhibits strong resistance against common cryptanalytic attacks.
The encryption process is highly efficient, suitable for practical applications.

Conclusions:

The developed image encryption method effectively overcomes the limitations of existing systems.
It offers a secure, efficient, and robust solution for digital image protection.
The integration of Hopfield neural networks and chaotic systems provides a powerful framework for cryptographic applications.