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Dual-Region Encryption Model Based on a 3D-MNFC Chaotic System and Logistic Map.

Jingyan Li1, Yan Niu1, Dan Yu1

  • 1College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China.

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Summary
This summary is machine-generated.

This study introduces a novel dual-region encryption model for portrait images, enhancing security and efficiency. The new method significantly improves encryption speed by processing facial and non-facial regions separately.

Keywords:
3D-MNFCCNN key generationDNA encodingMTCNN facial detectiondual-region encryptiondynamical analysissecurity analysis

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Area of Science:

  • Computer Science
  • Information Security
  • Image Processing

Background:

  • Facial information privacy is critical, necessitating secure encryption methods.
  • Traditional image encryption is inefficient, processing entire images regardless of sensitive content.
  • Existing methods often impose unnecessary computational burdens on portrait image security.

Purpose of the Study:

  • To develop an efficient and secure dual-region encryption model for portrait images.
  • To address the inefficiency of traditional full-image encryption methods.
  • To protect sensitive facial information while optimizing computational resources.

Main Methods:

  • Utilized Multi-task Cascaded Convolutional Network (MTCNN) for facial and non-facial region segmentation.
  • Developed a robust encryption scheme for facial regions using a CNN-based key generator, a 3D-MNFC chaotic system, DNA encoding, and bit reversal.
  • Employed the Logistic map with XOR operations for efficient encryption of non-facial regions.

Main Results:

  • Achieved a large key space (2^536) and near-ideal information entropy (7.9995).
  • Demonstrated high security with NPCR (99.6055%) and UACI (33.4599%) values.
  • Improved encryption efficiency by at least 37.82% compared to traditional methods.

Conclusions:

  • The proposed dual-region encryption model offers a significant improvement in efficiency and security for portrait images.
  • This approach effectively balances the need for robust protection of sensitive facial data with computational efficiency.
  • The model provides a practical solution for secure and fast image encryption in privacy-sensitive applications.