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    Area of Science:

    • Optical physics
    • Information security
    • Machine learning

    Background:

    • Computational ghost imaging (CGI) offers unique encryption capabilities.
    • Fractional orbital angular momentum (FOAM) enables high-dimensional optical data transmission.
    • Secure optical image transmission remains a critical challenge.

    Purpose of the Study:

    • To develop a novel, secure method for optical image encryption and transmission.
    • To integrate CGI, FOAM, and deep learning for enhanced security and data capacity.
    • To establish a robust authentication-decryption mechanism for optical signals.

    Main Methods:

    • Image encryption using CGI by reindexing bucket signals and converting reference signals to sparse sequences.
    • Encoding encrypted signals onto FOAM modes with a rotational phase shift for secure transmission.
    • Utilizing a pre-trained DenseNet for decryption by identifying FOAM topological charges from intensity patterns.
    • Implementing an authentication mechanism prior to decryption to ensure signal legality.

    Main Results:

    • Successful encryption and secure transmission of optical images using the combined CGI-FOAM approach.
    • Accurate decryption of images via DenseNet, demonstrating the effectiveness of the proposed method.
    • Enhanced security through rotational phase shifts and an integrated authentication-decryption process.
    • Demonstrated feasibility of interdisciplinary research between optical communication and information security.

    Conclusions:

    • The proposed method effectively combines CGI and FOAM for secure optical image encryption and transmission.
    • Deep learning significantly aids in the decryption process, improving accuracy and efficiency.
    • The integrated authentication mechanism provides an additional layer of security against unauthorized access.
    • This research opens new avenues for secure OAM-based optical communication and CGI-based information security.