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Steganography Based on High-Dimensional Reference Table

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Book cover Digital-Forensics and Watermarking (IWDW 2014)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9023))

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Abstract

In this paper, a high dimensional reference table-based embedding is proposed for steganography. It employs an \(N\)-length cover vector to embed \(B(K,N)\)-ary notational digits, which causes a Euclidean distance-based distortion no more than \(K\). By choosing the appropriate values of \(K\) and \(N\), a embedding strategy suitable for arbitrary relative capacity can be achieved. Further, we introduce the One-by-One Search Algorithm (OOSA) to optimize the mapping function, which maps the elements in the codeword set to those in the neighborhood set. Due to the high dimensional embedding, the proposed scheme provides a higher embedding efficiency or relative capacity compared with other similar approaches. Experiments on both grayscale images and H.264 video show the effectiveness of the proposed scheme.

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Author information

Authors and Affiliations

  1. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Bingwen Feng, Wei Lu &  Lu Dai

  2. School of Software, Sun Yat-sen University, Guangzhou, 510006, China

    Wei Sun

Authors
  1. Bingwen Feng
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  2. Wei Lu
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  3. Lu Dai
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  4. Wei Sun
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Corresponding author

Correspondence to Wei Sun .

Editor information

Editors and Affiliations

  1. NJIT, Newark, New Jersey, USA

    Yun-Qing Shi

  2. Korea University, Seoul, Korea, Republic of (South Korea)

    Hyoung Joong Kim

  3. Universidad de La Laguna, La Laguna, Spain

    Fernando Pérez-González

  4. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Ching-Nung Yang

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Feng, B., Lu, W., Dai, ., Sun, W. (2015). Steganography Based on High-Dimensional Reference Table. In: Shi, YQ., Kim, H., Pérez-González, F., Yang, CN. (eds) Digital-Forensics and Watermarking. IWDW 2014. Lecture Notes in Computer Science(), vol 9023. Springer, Cham. https://doi.org/10.1007/978-3-319-19321-2_44

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  • DOI: https://doi.org/10.1007/978-3-319-19321-2_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19320-5

  • Online ISBN: 978-3-319-19321-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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