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Encryption and decryption applications on conductive films using THz real-time high-resolution imaging.

Wei Zhang, Chen Du, Bowen Tan

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    This study introduces a terahertz imaging system for secure data encryption using graphite pencil patterns. A novel deep learning denoising method enhances image clarity in low signal conditions, enabling reliable information decryption.

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    Area of Science:

    • Optics and Photonics
    • Information Security
    • Artificial Intelligence

    Background:

    • Terahertz (THz) imaging offers unique properties for secure applications.
    • Material-based encryption using THz transmission is feasible but challenged by low signal-to-noise ratio (SNR) imaging.
    • Existing denoising methods struggle with THz imaging clarity degradation.

    Purpose of the Study:

    • To develop a real-time THz quasi-near-field imaging system for low-cost, concealable information encryption and decryption.
    • To address the challenge of imaging clarity deterioration under ultra-low SNR conditions.
    • To enhance the stability and recognizability of encrypted information in noisy environments.

    Main Methods:

    • Designed three types of encryption structures (text, coating, QR code) using graphite pencils with distinct THz transmission properties.
    • Proposed a modified U-Net deep learning denoising method incorporating multi-frequency feature fusion and THz-specific physical priors.
    • Evaluated performance against the block-matching and 3D filtering (BM3D) algorithm using metrics like PSNR, SSIM, and FSIM.

    Main Results:

    • Successfully decrypted all designed encryption structures, validating material-based THz encryption.
    • The modified U-Net method significantly suppressed noise while preserving fine structural details, outperforming BM3D.
    • Encrypted information remained clearly recognizable even in complex noise environments, showing improved PSNR, SSIM, and FSIM values.

    Conclusions:

    • A unified framework combining material-based encryption, THz imaging, and deep learning denoising was established.
    • The proposed approach offers low implementation cost, strong concealment, and enhanced stability for THz imaging applications.
    • This work paves the way for secure communication, anti-counterfeiting, and information protection using THz technology.