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Reversible watermarking based on extreme prediction using modified differential evolution

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Abstract

Prediction-error expansion (PEE) is an important technique for reversible watermarking (RW). Traditional PEE methods exploit pixel correlations by modifying prediction-error histogram (PEH) with Laplacian-like distributions, but they rarely consider the influence of needless shifting on image distortion. Asymmetric PEE approaches utilize skewed PEHs to select fewer shifted pixels for superior performance in capacity-distortion control tasks. In this way, the construction of a suitable extreme predictor is especially useful for increasing embedding capacity and reducing image distortion to satisfy user requirements. However, in recent works, the parameters of extreme predictors were not fully optimized. This paper presents a novel extreme predictor, in which the prediction parameters are optimized by using a differential evolution (DE) algorithm to increase the performance of asymmetric PEE by building a sharp and skewed PEH. Specifically, we develop a new DE algorithm based on hybrid mutation and grouping crossover operations to further improve the optimization accuracy of the algorithm. Then, an RW scheme based on the proposed predictor and image separation is built to fully exploit the embedding potential of different bit-planes in images. To reduce image distortion, a strategy that is complementary to double-embedding is presented for lower bit-plane (LBP) embedding. Experimental results demonstrate that the proposed scheme can achieve better image quality and a larger embedding capacity than similar state-of-the-art methods.

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Acknowledgements

This work was supported by the Hainan Province Basic and Applied Basic Research Program High-level Talent Project (No. 2019RC044) and the Research Project of Education and Teaching Reform of Hainan University (No. hdjy2053).

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Authors and Affiliations

  1. School of Cyberspace Security, Hainan University, Haikou, 570228, China

    Yujian Zhuang, Sheng Liu, Changfeng Ding & Xiaoyi Zhou

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  1. Yujian Zhuang
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Correspondence to Xiaoyi Zhou.

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Zhuang, Y., Liu, S., Ding, C. et al. Reversible watermarking based on extreme prediction using modified differential evolution. Appl Intell 52, 14406–14425 (2022). https://doi.org/10.1007/s10489-022-03211-1

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