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Coupling Quantum Random Walks with Long- and Short-Term Memory for High Pixel Image Encryption Schemes.

Junqing Liang¹, Zhaoyang Song¹, Zhongwei Sun¹

¹School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China.

Entropy (Basel, Switzerland)

|February 25, 2023

Summary

This summary is machine-generated.

This study introduces a novel image encryption scheme using quantum random walks and long short-term memory (LSTM) networks. This method enhances pseudorandom matrix generation for secure, high-pixel-density image encryption.

Keywords:

high pixel density image encryption neural networks quantum random walk

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Area of Science:

Cryptography
Image Processing
Artificial Intelligence

Background:

Traditional encryption methods struggle with high pixel density images.
Quantum random walk algorithms face efficiency challenges in generating large pseudorandom matrices.

Purpose of the Study:

To propose an efficient and robust image encryption scheme for high pixel density images.
To leverage Long Short-Term Memory (LSTM) networks to improve quantum random walk algorithm efficiency and pseudorandom matrix quality.

Main Methods:

Integration of quantum random walk algorithm with LSTM networks for pseudorandom matrix generation.
Training LSTM networks with image data to predict highly random output matrices.
Generating an LSTM prediction matrix for image encryption based on pixel density.

Main Results:

Achieved high information entropy (average 7.9992).
Demonstrated strong pixel diffusion and confusion with high NPCR (average 99.6231%) and UACI (average 33.6029%).
Exhibited low correlation (average 0.0032) between adjacent pixels.

Conclusions:

The proposed scheme effectively encrypts high pixel density images.
The integration of LSTM with quantum random walks enhances pseudorandom matrix generation for robust encryption.
The encryption scheme shows resilience against noise and interference, suitable for real-world applications.