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Image authentication method based on Fourier zero-frequency replacement and single-pixel self-calibration imaging by

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    A novel diffractive deep neural network enables optical authentication using terahertz light. This system offers faster, automated image authentication, showcasing potential for integrated optical and machine learning applications.

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    Area of Science:

    • Optics
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Diffractive deep neural networks (DDNNs) integrate diffraction principles with neural networks for optical computation.
    • Optical authentication systems require efficient and automated methods for image verification.

    Purpose of the Study:

    • To develop a fully optical authentication model using a diffractive deep neural network.
    • To enhance authentication speed and automation through optical principles.

    Main Methods:

    • Utilized terahertz light propagation within a diffractive deep neural network framework.
    • Integrated a self-calibration single-pixel imaging model for comprehensive optical authentication.
    • Employed Fourier zero-frequency response and signal-to-noise ratio for image filtering and batch authentication.

    Main Results:

    • Demonstrated a fully optical authentication system with significantly faster authentication speeds.
    • Validated the system's strong automation performance through computer simulations.
    • Showcased the effectiveness of signal-to-noise ratio as a criterion for batch image authentication.

    Conclusions:

    • The proposed diffractive deep neural network-based optical authentication system achieves high speed and automation.
    • This approach offers a promising direction for combining diffractive deep neural networks with optical systems for authentication tasks.