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Optimal Distortion Estimation for Prediction Error Expansion Based Reversible Watermarking

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Digital Forensics and Watermarking (IWDW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10082))

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Abstract

Reversible Image Watermarking is a technique to losslessly embed and retrieve information (in the form of a watermark) in a cover image. Prediction Error Expansion based schemes are currently the most efficient and widely used reversible image watermarking techniques. Estimation of the minimum achievable (optimum) distortion for a given payload and a given cover image, is an important problem for reversible watermarking schemes. In this paper, we first show that the bounded capacity distortion minimization problem for prediction error expansion based reversible watermarking schemes is NP-hard, and that the corresponding decision version of the problem is NP-complete. We then propose a low computational overhead heuristic to estimate the minimal distortion. Our estimation technique first estimates (for a given image) the prediction error distribution function without explicit use of any prediction scheme, and then minimizes the distortion for a given payload by solving a convex optimization problem to estimate an embedding threshold parameter. Experimental results for common benchmark images closely match the predictions from our technique, and thus verify the consistency of our approach.

This work is supported by Science and Engineering Research Board (SERB), Govt. of India under Research Grant No. SB/FTP/ETA-0191/2013.

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Authors and Affiliations

  1. Secured Embedded Architecture Laboratory (SEAL), Department of Computer Science and Engineering Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    Aniket Roy & Rajat Subhra Chakraborty

Authors
  1. Aniket Roy
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  2. Rajat Subhra Chakraborty
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Correspondence to Aniket Roy .

Editor information

Editors and Affiliations

  1. New Jersey Institute of Technology, Newark, New Jersey, USA

    Yun Qing Shi

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

    Hyoung Joong Kim

  3. University of Vigo, Vigo, Spain

    Fernando Perez-Gonzalez

  4. Chinese Academy of Sciences, Beijing, China

    Feng Liu

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Roy, A., Chakraborty, R.S. (2017). Optimal Distortion Estimation for Prediction Error Expansion Based Reversible Watermarking. In: Shi, Y., Kim, H., Perez-Gonzalez, F., Liu, F. (eds) Digital Forensics and Watermarking. IWDW 2016. Lecture Notes in Computer Science(), vol 10082. Springer, Cham. https://doi.org/10.1007/978-3-319-53465-7_20

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  • DOI: https://doi.org/10.1007/978-3-319-53465-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53464-0

  • Online ISBN: 978-3-319-53465-7

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