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Parallel Binary Image Cryptosystem Via Spiking Neural Networks Variants.

Mingzhe Liu1, Feixiang Zhao1, Xin Jiang1

  • 1State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610051, P. R. China.

International Journal of Neural Systems
|February 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a parallel binary image encryption framework using spiking neural P (SNP) systems. This novel approach enhances efficiency and security for binary image cryptosystems through parallel processing.

Keywords:
Spiking neural P systemsbinary image encryptionparallel computing

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Traditional binary image encryption methods often suffer from inefficiencies due to serial processing.
  • Spiking Neural Networks (SNNs) offer potential for parallel computation, which can address these limitations.

Purpose of the Study:

  • To propose a novel parallel binary image encryption framework.
  • To enhance the efficiency and security of binary image cryptosystems.

Main Methods:

  • The proposed framework utilizes variants of Spiking Neural P (SNP) systems.
  • The permutation unit is designed using SNP systems with multiple channels and polarizations (SNP-MCP).
  • The diffusion unit is designed using SNP systems with astrocyte-like control (SNP-ALC).

Main Results:

  • The SNP-MCP-based permutation and SNP-ALC-based diffusion units enable parallel computing.
  • Theoretical analysis confirms the high efficiency of the proposed binary image cryptosystem.
  • Security analysis experiments demonstrate the robustness and security of the cryptosystem.

Conclusions:

  • The developed parallel framework based on SNP systems significantly improves binary image encryption efficiency.
  • The proposed cryptosystem offers a secure solution for binary image protection.