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An image compression-encryption algorithm based on BP neural network optimized with fireworks algorithm.

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This study introduces a novel image compression and encryption algorithm using an optimized BP neural network. The method enhances compression efficiency and reconstruction quality while ensuring robust security against attacks.

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Area of Science:

  • Computer Science
  • Information Security
  • Artificial Intelligence

Background:

  • Increasing image data volume poses storage and transmission challenges.
  • Existing algorithms struggle to balance compression efficiency with robust security.
  • Need for advanced methods in secure image compression.

Purpose of the Study:

  • To propose a novel algorithm for image compression and encryption.
  • To enhance compression efficiency and reconstruction quality using an optimized BP neural network.
  • To improve security through chaotic systems and advanced scrambling/diffusion techniques.

Main Methods:

  • Optimized Backpropagation (BP) neural network using Fireworks Algorithm (FWA) for compression.
  • Variable-parameter chaotic system (Logistic-Tent and Chebyshev maps) for pseudo-random sequence generation.
  • Multi-level global-local scrambling and Gray Code-based mutation-diffusion for encryption.

Main Results:

  • The optimized BP network improved compression efficiency and reconstruction quality.
  • The chaotic system and scrambling mechanisms provided robust security.
  • The proposed scheme outperformed standard BP networks in high-fidelity image recovery.

Conclusions:

  • The novel algorithm effectively balances image compression and encryption.
  • The integration of FWA-optimized BP networks and chaotic systems offers superior performance.
  • The method demonstrates strong robustness and attack resistance for secure image handling.