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    Area of Science:

    • Optics
    • Information Security
    • Artificial Intelligence

    Background:

    • Optical encryption offers high security and speed.
    • Complex-amplitude information (amplitude and phase) requires advanced encryption techniques.
    • Deep learning models can learn intricate mappings for data processing.

    Purpose of the Study:

    • To develop a speckle-based optical encryption scheme for complex-amplitude images.
    • To integrate deep learning for efficient encryption and decryption.
    • To demonstrate the feasibility and effectiveness of the proposed method.

    Main Methods:

    • Complex-amplitude plaintext modulation using superpixel coding and a digital micromirror device.
    • Speckle pattern generation via a scattering medium after a 4f system.
    • Training a Y-shaped convolutional network (Y-Net) for plaintext-ciphertext mapping.
    • Utilizing the trained Y-Net for ciphertext decryption.

    Main Results:

    • Successful encryption and decryption of complex-amplitude images.
    • High-quality extraction of amplitude and phase information from ciphertext.
    • Experimental validation of the proposed speckle encryption and deep learning integration.

    Conclusions:

    • The proposed speckle-based optical encryption scheme effectively handles complex-amplitude information.
    • Deep learning, specifically the Y-Net model, significantly enhances decryption speed and quality.
    • This integrated approach shows strong potential for secure optical information processing.