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Adaptive image camouflage using human visual system model

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A Correction to this article was published on 28 November 2018

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Abstract

The development of image decryption techniques means that some current encryption techniques that turn a secret image into a meaningless image are no longer safe. Recently, encrypting an image into another meaningful image has opened a new route to transmit the secret image. This paper proposes an improved image encryption approach to camouflage the secret image into another host image. The visual quality of the final camouflage image has been significantly improved by using a human visual system (HVS) model. First, the secret image is pre-encrypted with a specific image encryption method, e.g., chaotic mapping method, then the host image is decomposed by a lifting wavelet transform (LWT) to generate integral wavelet coefficients. The encrypted secret image is then adaptively adjusted to a narrower range to guarantee that the final camouflage image is within a reasonable interval. Next, by using a HVS model, each modified image pixel is embedded into the corresponding detailed coefficients through an adaptive allocation strategy. Specifically, for pixels with extremely high or low intensity or high texture complexity, more bits of the modified secret pixels are allocated to substitute for the corresponding bits of coefficients. Then the modified coefficients are retransformed back to generate the final camouflage image. The secret image can be losslessly recovered through reversal procedures. The experimental results demonstrate the efficacy of the proposed method.

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  • 28 November 2018

    The expression J’ in pages 4, 6, 9, 12 and 13 of the original publication were incorrectly written as J where the symbol prime (‘) was missing. Also the expression Pi,j found in page 5 of the original publication was incorrectly written as Pi,j.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61702332, 61672354, 61562007), Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security(MIMS16-03), the Guangxi Natural Science Foundation (2017GXNSFAA198222), the Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing. The authors would like to thank the anonymous reviewers for their helpful comments.

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Authors and Affiliations

  1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Shanghai, 200093, China

    Heng Yao, Chuan Qin & Ying Tian

  2. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, China

    Heng Yao & Zhenjun Tang

  3. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Xiaokai Liu

Authors
  1. Heng Yao
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  2. Xiaokai Liu
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  3. Zhenjun Tang
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  4. Chuan Qin
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  5. Ying Tian
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Correspondence to Chuan Qin.

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The original version of this article was revised: The expression J’ in pages 4, 6, 9, 12 and 13 of the original publication were incorrectly written as J where the symbol prime (’ ) was missing. Also the expression Pi,j found in page 5 of the original publication was incorrectly written as Pi,j.

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Yao, H., Liu, X., Tang, Z. et al. Adaptive image camouflage using human visual system model. Multimed Tools Appl 78, 8311–8334 (2019). https://doi.org/10.1007/s11042-018-6813-8

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  • DOI: https://doi.org/10.1007/s11042-018-6813-8

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