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Enhanced image encryption with deep generative models using a self-attention mechanism.

Ilham Karmouni1, Nawal El Ghouate1, Mohamed Amine Tahiri2

  • 1Engineering, Systems and Applications Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco.

Scientific Reports
|April 2, 2026
PubMed
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This summary is machine-generated.

This study introduces a novel deep learning system for secure image encryption. The model enhances security and accuracy for medical images, improving visual data protection.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cryptography

Background:

  • Digital image security is crucial across various applications.
  • Existing encryption methods face challenges in speed and security.
  • Deep generative models offer potential for advanced image encryption.

Purpose of the Study:

  • To develop a novel image encryption system using deep generative models and self-attention.
  • To enhance both encryption and decryption speeds.
  • To improve the security and reconstruction accuracy of encrypted images.

Main Methods:

  • Utilized CycleGAN-based models for image encryption and decryption.
  • Incorporated a self-attention module to capture global image dependencies.
  • Evaluated performance on color image datasets, brain MRI, and skin cancer images.
Keywords:
CycleGANDeep generative modelsMHSAMedical image encryptionRobust visual security

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Main Results:

  • Achieved high security with Entropy ≈ 7.9996 and NPCR ≈ 99.99%.
  • Demonstrated high reconstruction accuracy (SSIM ≈ 0.99, PSNR > 40 dB).
  • Showcased strong resistance to differential (UACI ≈ 33.46) and occlusion attacks.

Conclusions:

  • The proposed system offers a secure and efficient solution for image encryption.
  • Deep generative models with self-attention present promising avenues for visual cryptography.
  • The system's effectiveness is validated on critical medical imaging datasets.