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    Area of Science:

    • Optics and Photonics
    • Information Security
    • Materials Science

    Background:

    • Traditional 2D optical masks are vulnerable to duplication and mathematical formulation.
    • Advanced security measures are needed to combat sophisticated counterfeiting techniques.
    • Three-dimensional (3D) optical phase masks offer enhanced security due to their complexity.

    Purpose of the Study:

    • To develop and validate a novel authentication system utilizing 3D optical phase masks.
    • To explore the potential of 3D optical phase codes (OPCs) for secure identification.
    • To assess the feasibility of using speckle pattern analysis for authentication.

    Main Methods:

    • A random 3D optical phase mask was used to modulate a quick response code, generating a 3D optical phase code (OPC).
    • Light scattering through the 3D OPC produced a unique speckle pattern, recorded by a CCD device.
    • Feature extraction involved calculating statistical parameters (mean, variance, skewness, kurtosis, entropy) of the speckle patterns.
    • A random forest classifier was employed for the authentication process.

    Main Results:

    • The 3D OPC generated unique speckle patterns dependent on the mask's material and structure.
    • Statistical analysis of speckle patterns provided distinct features for identification.
    • The random forest classifier successfully authenticated objects based on the extracted features.
    • Optical experiments confirmed the viability of the proposed authentication scheme.

    Conclusions:

    • The proposed authentication scheme based on 3D optical phase masks offers a robust and secure method.
    • The complexity of 3D OPCs significantly enhances security against duplication compared to 2D methods.
    • Speckle pattern analysis combined with machine learning provides a feasible approach for object authentication.