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A Multi-Image Encryption Based on Sinusoidal Coding Frequency Multiplexing and Deep Learning.

Qi Li1, Xiangfeng Meng1, Yongkai Yin1

  • 1School of Information Science and Engineering and Shandong Provincial Key Laboratory of Laser Technology and Application, Shandong University, Qingdao 266237, China.

Sensors (Basel, Switzerland)
|September 28, 2021
PubMed
Summary
This summary is machine-generated.

A novel optical encryption method uses sinusoidal stripe coding and deep learning to encrypt multiple images efficiently. This technique significantly enhances security and decryption speed for large numbers of images.

Keywords:
deep learningfrequency multiplexingoptical information securitysinusoidal coding

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Area of Science:

  • Optical encryption technology
  • Digital image processing
  • Machine learning applications

Background:

  • Traditional multi-image encryption methods are limited in capacity.
  • There is a need for efficient and robust multi-image encryption solutions.

Purpose of the Study:

  • To propose a new multi-image encryption method using sinusoidal stripe coding frequency multiplexing and deep learning.
  • To enhance the number of images that can be encrypted and improve decryption efficiency.

Main Methods:

  • Grouping images and encoding each with a random matrix.
  • Modulating images with specific sinusoidal stripes for frequency domain separation.
  • Superimposing and scrambling groups to generate ciphertext.
  • Utilizing deep learning for direct plaintext image reconstruction.

Main Results:

  • Successfully encrypted 32 images with a correlation coefficient (CC) exceeding 0.99.
  • Demonstrated efficiency through histogram analysis, adjacent pixel correlation, and anti-attack analyses.
  • Achieved high robustness against noise and occlusion attacks.

Conclusions:

  • The proposed method offers a significant improvement in multi-image encryption capacity.
  • The integration of deep learning ensures fast and high-quality decryption.
  • The technique exhibits excellent robustness and security for practical applications.