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Generic Reversible Visible Watermarking via Regularized Graph Fourier Transform Coding.

Wenfa Qi, Sirui Guo, Wei Hu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 16, 2021
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    Summary
    This summary is machine-generated.

    This study introduces a versatile reversible visible watermarking (RVW) framework. It enables copyright protection and image restoration, outperforming existing methods with higher visual quality.

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    Area of Science:

    • Digital Image Processing
    • Computer Vision
    • Information Security

    Background:

    • Reversible visible watermarking (RVW) protects copyright by embedding patterns and allowing original image recovery.
    • Current RVW algorithms lack versatility, being tied to specific watermarking schemes.

    Purpose of the Study:

    • To propose a generic RVW framework adaptable to various visible watermarking schemes.
    • To enhance the versatility and efficiency of reversible visible watermarking.

    Main Methods:

    • Developed a generic RVW framework using a reconstruction data packet (compressed difference image).
    • Employed regularized Graph Fourier Transform (GFT) coding for efficient compression of the difference image.
    • Utilized graph Laplacian regularizer for smoothing and multi-resolution GFTs for encoding.

    Main Results:

    • The proposed framework demonstrates superior versatility compared to state-of-the-art methods.
    • Achieved higher visual quality of watermarked images due to minimal auxiliary data embedding.
    • Successfully enabled blind recovery of the host image.

    Conclusions:

    • The generic RVW framework offers a versatile solution for copyright protection.
    • Regularized GFT coding is effective for compressing difference images in RVW.
    • The approach enhances both security and visual fidelity in digital watermarking.