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A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

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Optical Encryption Using Attention-Inserted Physics-Driven Single-Pixel Imaging.

Wen-Kai Yu¹, Shuo-Fei Wang¹, Ke-Qian Shang¹

¹Center for Quantum Technology Research, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement of Ministry of Education, School of Physics, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|February 10, 2024

Summary

This summary is machine-generated.

This study introduces an attention-inserted physics-driven neural network for optical encryption using single-pixel imaging (SPI). This novel approach eliminates pre-training, enabling faster, more adaptable, and secure image encryption.

Keywords:

attention module image reconstruction optical encryption physics-driven neural network single-pixel imaging

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Area of Science:

Optics and Photonics
Computer Science
Cryptography

Background:

Deep learning has advanced optical encryption but requires extensive training and retraining.
Current methods face limitations in adaptability and computational efficiency for dynamic scenes.

Purpose of the Study:

To develop a novel single-pixel imaging (SPI) encryption scheme that overcomes the limitations of traditional deep learning approaches.
To introduce an attention-inserted physics-driven neural network for efficient and secure optical encryption.

Main Methods:

An attention module encrypts image data and cryptographic keys into a 1D ciphertext signal.
A physics-driven neural network decodes the ciphertext for high-fidelity decryption.
The scheme integrates spatial modulation freedom and eliminates network pre-training.

Main Results:

Demonstrated feasibility through simulations and experiments.
Achieved high-fidelity decryption without prior network training.
Showcased eavesdropping resistance for enhanced security.

Conclusions:

The proposed scheme offers an intelligent and adaptable solution for SPI-based optical encryption.
This approach reduces computational overhead and training time.
It paves the way for more advanced, deep-learning-integrated optical security systems.