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Novel zero-watermarking method using the compressed sensing significant feature

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Abstract

In this paper, we proposed a novel zero- watermarking method using the compressed sensing (CS) significant feature. The original image is segmented into non-overlapping blocks and then constructs a scrambled block Hadamard ensemble (SBHE) matrix as a sensing matrix to sense every block. CS significant feature can be represented by the sum of each block’s measurements. Then each pixel value of the binary zero-watermark is embedded by modifying the CS significant features. At last zero-watermark is registered, and the sensing matrix is stored as extra key. With the CS significant feature, the robustness of the zero-watermarking algorithm can be greatly improved. SBHE matrix can reduce the computational complexity of the compressed sensing and also reduce storage costs. In addition, because of the randomness of SBHE matrix, the proposed zero-watermarking method can provide excellent confidentiality. Moreover, our algorithm is different from the traditional zero-watermarking methods with pure meaningless image features, copyright holders can be legally recognized by the identity’s image which is embedded into the zero-watermark, so the authentication process of the zero-watermark is more intuitive and convenient.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their great efforts and valuable comments that are greatly helpful to improve the clarity and quality of this manuscript. Special thanks are also due to the instrumental and data analysis from Analytical and Testing Center, Northeastern University. This work was supported by “985 Project” of Northeastern University (No. 985-3-DC-F24), National Natural Science Foundation of China (No. 61202446), and Fundamental Research Funds for the Central Universities (N150404004).

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  1. College of Computer Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning, China

    Jun Lang & Chunlei Ma

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  1. Jun Lang
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  2. Chunlei Ma
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Correspondence to Jun Lang.

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Lang, J., Ma, C. Novel zero-watermarking method using the compressed sensing significant feature. Multimed Tools Appl 82, 4551–4567 (2023). https://doi.org/10.1007/s11042-022-13601-9

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