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Networking Communication and Data Knowledge Engineering pp 115–126Cite as

Lagrangian Twin SVR Based Grayscale Image Watermarking Using LWT-QR Decomposition

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 4))

Abstract

A novel approach of image watermarking using Lagrangian twin support vector regression (LTSVR) and combination of a variant of wavelet transform and QR decomposition for copyright protection application is proposed in this chapter. Firstly, host image decomposed into low-frequency subband (LL) and detail subbands by applying lifting wavelet transform (LWT). Secondly, the blocks of LL depend on the fuzzy entropy, and the selected regions of LL are transformed using QR factorization. Then, image dataset is formed using the elements of matrix R (called feature vector) of each selected block. This image dataset acts input to LTSVR to find the function approximation which defines the relationship between the input and target. The scrambled bits of the binary watermark are inserted into the predicted values obtained through the trained LTSVR upon comparing with the target value. The scrambled bits are obtained by applying Arnold transformation, which provide security to the proposed approach. Experimental results using various kinds of images and comparison of existing methods prove that the proposed approach is highly imperceptible and robust.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, UIET, Maharishi Dayanand University, Rohtak, India

    Ashok Kumar Yadav &  Raj Kumar

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, New Delhi, India

    Rajesh Mehta

Authors
  1. Ashok Kumar Yadav
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  2. Rajesh Mehta
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  3. Raj Kumar
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Corresponding author

Correspondence to Ashok Kumar Yadav .

Editor information

Editors and Affiliations

  1. University of Murcia, Murcia, Spain

    Gregorio Martinez Perez

  2. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

  3. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  4. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Munesh C. Trivedi

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Yadav, A.K., Mehta, R., Raj Kumar (2018). Lagrangian Twin SVR Based Grayscale Image Watermarking Using LWT-QR Decomposition. In: Perez, G., Mishra, K., Tiwari, S., Trivedi, M. (eds) Networking Communication and Data Knowledge Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 4. Springer, Singapore. https://doi.org/10.1007/978-981-10-4600-1_11

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  • DOI: https://doi.org/10.1007/978-981-10-4600-1_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4599-8

  • Online ISBN: 978-981-10-4600-1

  • eBook Packages: EngineeringEngineering (R0)

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