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Related Experiment Video

Updated: Aug 30, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

486

A Novel Image Encryption Algorithm Based on Improved Arnold Transform and Chaotic Pulse-Coupled Neural Network.

Jinhong Ye1,2, Xiangyu Deng1,2, Aijia Zhang1,2

  • 1College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China.

Entropy (Basel, Switzerland)
|August 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel image encryption algorithm using a chaotic pulse-coupled neural network and improved Arnold transform for secure data transmission. The method enhances image security against various attacks.

Keywords:
Arnold transformchaotic pulse-coupled neural networkchaotic sequenceimage encryptionimage scrambling

Related Experiment Videos

Last Updated: Aug 30, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

486

Area of Science:

  • Computer Science
  • Information Security
  • Cryptography

Background:

  • Information security is critical in the digital age, with secure image transmission and storage being key research areas.
  • Existing image encryption methods face challenges in achieving high security and resistance to attacks.

Purpose of the Study:

  • To propose a novel image encryption algorithm to enhance the security of image data transmission.
  • To improve the scrambling degree and encryption effect of image data.

Main Methods:

  • Introducing oscillatory reset voltage into an uncoupled impulse neural network to induce chaotic characteristics.
  • Generating a chaotic sequence for pre-encryption via XOR operation with the image.
  • Applying an improved Arnold transform for scrambling the pre-encrypted image to create the final ciphertext.

Main Results:

  • The proposed algorithm demonstrates a superior encryption effect compared to existing methods.
  • Quantitative evaluations confirm the algorithm's high sensitivity to keys and plaintexts.
  • The algorithm exhibits a large key space and effective resistance to differential, noise, and clipping attacks.

Conclusions:

  • The developed image encryption algorithm offers enhanced security for image data transmission.
  • The combination of chaotic neural networks and Arnold transform provides a robust solution for information security challenges.
  • The algorithm's resilience makes it suitable for protecting sensitive image information.