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Information security scheme using deep learning-assisted single-pixel imaging and orthogonal coding.

Xiaogang Wang, Shanshan Lin, Jidong Xue

|February 14, 2023

Summary

This summary is machine-generated.

This study introduces a novel hybrid method for secure optical image transmission using deep learning-assisted single-pixel imaging (SPI) and orthogonal coding. The technique efficiently compresses, encrypts, and reconstructs multiple images, enhancing information security.

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Area of Science:

Information Security
Optical Imaging
Deep Learning

Background:

Secure and efficient transmission of multiple optical images is a critical challenge in information security.
Existing methods may lack efficiency or robust security for multi-user access.

Purpose of the Study:

To develop a hybrid scheme for optical image compression, encryption, and reconstruction.
To enable secure, efficient, and multi-user access to optical image data.

Main Methods:

Utilizing deep learning-assisted single-pixel imaging (SPI) for encryption.
Employing orthogonal coding for signal compression and ciphertext generation.
Leveraging a DenseNet-based deep learning framework for image reconstruction.

Main Results:

The proposed scheme successfully encrypts 2D images into 1D signals, compresses them via binarization, and secures them with orthogonal coding.
Orthogonal keys enable decoding of ciphertext back to binarized signals.
Deep learning enhances reconstruction efficiency and quality, verified by numerical and experimental results.

Conclusions:

The hybrid SPI and orthogonal coding scheme offers a feasible and effective solution for secure and efficient multi-image transmission.
Deep learning integration significantly improves image reconstruction.
The method addresses key challenges in optical image information security.