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Efficient capacity-distortion reversible data hiding based on combining multipeak embedding with local complexity

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Abstract

Recently, most histogram shifting-based reversible data hiding (RDH) algorithms have considered the impact of textural information on embedding performance, while exploiting pixels with different local complexities. Prioritizing pixels with small local complexities to accommodate secret data decreases the invalid shifting pixels, thereby reducing distortion. However, though effective, the local complexity is not calculated precisely enough, which results in inaccurate texture division and does not considerably reduce distortion. Thus, we employ a novel local complexity calculation and multipeak embedding (MPE) to effectively improve the capacity-distortion performance. Specifically, the host image is first preprocessed by the dot-cross pattern and divided into two subsets. Then the pixel local complexity of each subset is computed by using the spatial correlation of pixels (SCOP) to improve calculation accuracy. Finally, the peak bins to be expanded in the regions with lower local complexity are adaptively selected for embedding with secret data by MPE. To ensure that the authorized operator can securely gain error-free secret data, we design the location index sequences as special keys, which guarantee the algorithm reversibility and enhance the security of the algorithm at the same time. Experimental results show that our algorithm has superior embedding effectiveness compared to some state-of-the-art RDH methods.

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Acknowledgments

All the authors are deeply grateful to the editors for smooth and fast handling of the manuscript. The authors would also like to thank the anonymous referees for their valuable suggestions to improve the quality of this paper. This work is supported by the National Natural Science Foundation of China (Grant No. 61802111, 61872125) and Science and Technology Foundation of Henan Province of China (Grant No. 182102210027, 182102410051) and the Key Science and Technology Project of Henan Province (Grant No. 201300210400, 212102210094).

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Authors and Affiliations

  1. School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China

    Zhifeng Fu, Mengxin Gong, Guoqiang Long, Xiuli Chai & Yang Lu

  2. Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, China

    Zhifeng Fu, Mengxin Gong, Guoqiang Long, Xiuli Chai & Yang Lu

  3. School of Software, Intelligent Data Processing Engineering Research Center of Henan Province, Institute of Intelligent Network System, Henan University, Kaifeng, 475004, China

    Zhihua Gan

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  1. Zhifeng Fu
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Fu, Z., Gong, M., Long, G. et al. Efficient capacity-distortion reversible data hiding based on combining multipeak embedding with local complexity. Appl Intell 52, 13006–13026 (2022). https://doi.org/10.1007/s10489-022-03323-8

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