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Updated: Sep 16, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Flexible visually secure image encryption with meta-learning compression and chaotic systems.

Wei Chen1, Yichuan Wang2, Cheng Shi1

  • 1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel image encryption scheme using meta-learning and a chaotic system for secure, high-quality visual data. The flexible method balances running time and image quality for broad applications.

Keywords:
Chaotic systemDynamic auxiliary inputImage encryptionMeta-learning compressionTraditional deep learning

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Area of Science:

  • Computer Science
  • Information Security
  • Artificial Intelligence

Background:

  • Growing demand for secure digital image encryption across various applications.
  • Limitations of existing methods include insufficient security and poor decrypted image quality.
  • Need for advanced techniques integrating compression and encryption for visual data.

Purpose of the Study:

  • To propose a flexible and secure image encryption scheme.
  • To enhance decrypted image quality and encryption security.
  • To leverage meta-learning, chaotic systems, and deep learning for image security.

Main Methods:

  • Developed a meta-learning compression reconstruction network with dynamic auxiliary input for high-quality image compression.
  • Constructed a novel IS-DP chaotic system by combining 2D-IS chaotic system with a deep learning network for image encryption.
  • Implemented a lossless LSB-2^k correction embedding method for embedding the secret image into a carrier image.

Main Results:

  • Achieved high-quality compression and visually secure encryption of digital images.
  • Demonstrated the effectiveness of the proposed IS-DP chaotic system and meta-learning approach.
  • Validated the feasibility of deep learning methods in integrated encryption and compression tasks.

Conclusions:

  • The proposed scheme offers a flexible solution for visually secure image encryption.
  • Meta-learning provides adaptability, allowing users to balance performance and quality.
  • The integration of deep learning and chaotic systems shows significant potential for advanced image security applications.