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Optical image conversion and encryption based on structured light illumination and a diffractive neural network.

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    This study introduces a novel optical image encryption method using structured light and a diffractive neural network (DNN). The technique converts images, offering enhanced security and a large key space for robust data protection.

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    Area of Science:

    • Optics and Photonics
    • Information Security
    • Computational Imaging

    Background:

    • Traditional optical encryption methods face challenges in security and efficiency.
    • Diffractive neural networks (DNNs) offer a novel approach for optical information processing.
    • Structured light illumination provides unique encoding capabilities.

    Purpose of the Study:

    • To propose and demonstrate a new optical image encryption method.
    • To achieve secure image conversion using a diffractive neural network.
    • To enhance concealment and security through a large key space.

    Main Methods:

    • Utilizing structured light illumination and a diffractive neural network (DNN).
    • Encoding plaintext images into ciphertext using a structured phase mask (SPM) and phase-only masks (POMs) within the DNN.
    • Employing an iterative phase retrieval algorithm for image encoding.

    Main Results:

    • The proposed method successfully converts images, with ciphertext appearing as a visible image for enhanced concealment.
    • Numerical simulations confirmed the method's high feasibility, safety, and robustness.
    • The encryption scheme incorporates multiple parameters as security keys, creating a large key space.

    Conclusions:

    • The developed optical image encryption method based on DNNs provides a secure and robust solution for image data protection.
    • The image conversion capability enhances concealment, making it suitable for sensitive information.
    • The method's reliance on multiple security keys ensures a high level of security against unauthorized access.