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Synthetic Disvision of Polynomials

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Related Experiment Videos

Deep CNN chaotic key generator for multi-parameter elliptic curves over cybersecurity image encryption application.

Aliaa M Alabdali1, Esam A A Hagras2

  • 1Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, P. O. Box 344, 21911, Rabigh, Saudi Arabia. amalabdali@kau.edu.sa.

Scientific Reports
|June 25, 2026
PubMed
Summary

This study introduces a novel chaotic image encryption technique using Deep Convolutional Neural Networks Chaotic Key Generator and Multi-Parameter Multi-Prime Elliptic Curves (Deep-CNN-CKG MP-MP-EC). The method enhances image security through dynamic S-boxes and permutation tables derived from chaotic maps and elliptic curves.

Keywords:
Chaotic mapElliptic curvesImage encryptionS-box

Related Experiment Videos

Area of Science:

  • Cryptography and Information Security
  • Applied Mathematics
  • Computer Vision

Background:

  • Traditional image encryption methods face challenges in achieving high security and efficiency.
  • Chaotic systems offer potential for generating complex, unpredictable sequences crucial for secure encryption.
  • Elliptic curve cryptography provides a robust framework for secure key exchange and data protection.

Purpose of the Study:

  • To develop an advanced chaotic image encryption technique leveraging Deep Convolutional Neural Networks (Deep-CNN) and Elliptic Curves (EC).
  • To enhance the unpredictability and security of the encryption key stream generation.
  • To create dynamic substitution boxes (S-boxes) and permutation tables for a more robust encryption scheme.

Main Methods:

  • Utilizing Deep Convolutional Neural Networks (Deep-CNN) for chaotic key stream generation.
  • Employing a novel cascaded 2D sine-cosine cross-chaotic map to generate parameters for multi-parameter multi-prime elliptic curves (MP-MP-EC).
  • Dynamically generating non-linear substitution boxes (NL-S boxes) and permutation tables based on elliptic curve points and chaotic parameters.

Main Results:

  • The proposed Deep-CNN-CKG MP-MP-EC method generates an unpredictable key stream, enhancing encryption security.
  • Dynamic NL-S boxes and permutation tables improve the algorithm's resistance to cryptanalytic attacks.
  • Security analysis and simulations confirm the effectiveness and robustness of the proposed image encryption scheme.

Conclusions:

  • The developed chaotic image encryption technique offers a reliable and robust solution for secure image transmission.
  • The integration of Deep-CNN, chaotic maps, and elliptic curves provides a strong foundation for advanced cryptographic applications.
  • The proposed method demonstrates significant potential for practical implementation in secure image encryption systems.