<p>Due to the fact that most existing digital watermarking schemes can only add watermark information once, repeated additions will overwrite the original watermark information, making it impossible to achieve multi-level traceability of data. In response to the above issues, this paper proposes a multi-level database image watermark embedding scheme (MIWC) based on the Chinese remainder theorem. Taking image watermarks as carriers, MIWC preprocesses images using methods such as Haar wavelet transform and Bloom filter to form a pixel. It then performs secret segmentation on watermark information in accordance with the properties of the Chinese Remainder Theorem, enabling hierarchical and item-by-item addition of the watermark information. Furthermore, it records and traces the entire data flow chain. Functional analysis demonstrates that MIWC possesses the multi-level watermark embedding capability that existing schemes lack, thus holding high practical application value. Experimental results indicate that MIWC is reversible, with both watermark embedding and extraction exhibiting high efficiency. Even with the embedding of multi-level watermarks, MIWC remains highly efficient and lightweight. Meanwhile, MIWC also demonstrates strong robustness, being capable of resisting geometric attacks (including rotation and scaling) and common attacks (including JPEG compression, JPEG2000, Gaussian white noise, and salt-and-pepper noise) targeting the watermark.</p>

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A strong robust multi level database image watermark embedding scheme based on the Chinese remainder theorem

  • Huizheng Geng,
  • Sixu Guo,
  • Yingqing Liu,
  • Chunmei Li

摘要

Due to the fact that most existing digital watermarking schemes can only add watermark information once, repeated additions will overwrite the original watermark information, making it impossible to achieve multi-level traceability of data. In response to the above issues, this paper proposes a multi-level database image watermark embedding scheme (MIWC) based on the Chinese remainder theorem. Taking image watermarks as carriers, MIWC preprocesses images using methods such as Haar wavelet transform and Bloom filter to form a pixel. It then performs secret segmentation on watermark information in accordance with the properties of the Chinese Remainder Theorem, enabling hierarchical and item-by-item addition of the watermark information. Furthermore, it records and traces the entire data flow chain. Functional analysis demonstrates that MIWC possesses the multi-level watermark embedding capability that existing schemes lack, thus holding high practical application value. Experimental results indicate that MIWC is reversible, with both watermark embedding and extraction exhibiting high efficiency. Even with the embedding of multi-level watermarks, MIWC remains highly efficient and lightweight. Meanwhile, MIWC also demonstrates strong robustness, being capable of resisting geometric attacks (including rotation and scaling) and common attacks (including JPEG compression, JPEG2000, Gaussian white noise, and salt-and-pepper noise) targeting the watermark.