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Intelligent Fuzzy Approach Based High-Performance Steganography in Wavelet Domain

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Abstract

This paper proposes a high-performance steganographic technique in wavelet domain. This algorithm firstly encrypts the information to be embedded and then performs bright-color separation and wavelet transform on the cover-image. With the purpose of guaranteeing the perceptual quality of stego-image and improving the information hiding capacity of cover-image, wavelet trees of cover images are classified into three categories in accordance with human visual system (HVS) characteristics and fuzzy theory. Afterwards, the number of bits to be embedded in each high-frequency coefficient of a wavelet tree is varied adaptively with the type of the trees. Finally following the inverse wavelet transform, the stego-image is generated. Experimental results show that the proposed method is characterized by excellent performances in terms of image quality and message capacity while compatible with JPEG2000 standard. In addition, this algorithm does not need to refer to the original cover-image when the secret information is extracted and achieves blind detection of the extracted information.

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Funding

This work was supported by National Natural Science Foundation of China under contract no. 61803178.

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Authors and Affiliations

  1. School of Management Science and Engineering, Shandong University of Finance and Economics, 250014, Jinan, P.R. China

    Wenjin Wang & Xinghua Liu

  2. School of Finance, Shandong University of Finance and Economics, 250014, Jinan, P.R. China

    Miaomiao Lu

  3. School of Statistics, Shandong University of Finance and Economics, 250014, Jinan, P.R. China

    Jia Liu

  4. School of Electrical Engineering, University of Jinan, 250022, Jinan, P.R. China

    Ping Jiang

Authors
  1. Wenjin Wang
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  2. Xinghua Liu
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  3. Miaomiao Lu
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  4. Jia Liu
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Corresponding author

Correspondence to Wenjin Wang.

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Wenjin Wang, Liu, X., Lu, M. et al. Intelligent Fuzzy Approach Based High-Performance Steganography in Wavelet Domain. Aut. Control Comp. Sci. 56, 189–197 (2022). https://doi.org/10.3103/S0146411622030087

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  • DOI: https://doi.org/10.3103/S0146411622030087

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