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A Robust Watermarking Scheme Using Machine Learning Transmitted Over High-Speed Network for Smart Cities

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Security in Smart Cities: Models, Applications, and Challenges
  • The original version of this chapter was revised: The incorrect affiliation of author “Ankur Rai” has now been replaced with the correct one. The correction to this chapter is available at https://doi.org/10.1007/978-3-030-01560-2_16

Abstract

Medical images are more typical than any other ordinary images. In telemedicine applications, transmission of medical image via open channel, demands strong security and copyright protection. This paper discusses a safe and secure watermarking technique using a machine learning algorithm. In this paper, propagation of watermarked image is simulated over a 3GPP/LTE downlink physical layer. In our proposed robust watermarking model, a double layer security is introduced to ensure the robustness of embedded data and then a transform domain-based hybrid watermarking technique, embeds the scrambled data into the transform coefficients of the cover image. Support Vector Machine (SVM) is work as a classifier, which classify a medical image into two distinct areas i.e. Non-Region of Interest (NROI) and Region of Interest (ROI). The secure watermark information is embedded into the unimportant part of the medical image, using the proposed embedding algorithm. The objective of the projected model is to avoid any quality degradation to the medical image. The result achieved in this experiment reveal that 10−6 Bit Error Rate (BER) value is realizable for greater value of Signal to Noise Ratio (SNR) i.e. more than 10.4 dB of SNR. The Peak Signal to Noise Ratio (PSNR) of received cover image is more than 35 dB, which is acceptable for clinical applications.

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Change history

  • 28 December 2019

    In the original version of the book, the following belated correction has been incorporated: The affiliation of Ankur Rai has been changed from “Kamla Nehru Institute of Technology, Sultanpur 228118, India” to “Invertis University, Bareilly, Uttar Pradesh, India”. The erratum chapter and the book have been updated with the change.

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Author information

Authors and Affiliations

  1. Invertis University, Bareilly, Uttar Pradesh, India

    Ankur Rai

  2. Kamla Nehru Institute of Technology, Sultanpur, U.P., India

    Harsh Vikram Singh

Authors
  1. Ankur Rai
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  2. Harsh Vikram Singh
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Corresponding author

Correspondence to Ankur Rai .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information, Mansoura University, Dakahlia, Egypt

    Mohamed Elhoseny

  3. Department of Computer Science, Georgia Southern University, Statesboro Campus, Statesboro, GA, USA

    Syed Hassan Ahmed

  4. Department of Computer Science and Engineering, National Institute of Technology Patna, Patna University Campus, Patna, Bihar, India

    Amit Kumar Singh

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Rai, A., Singh, H.V. (2019). A Robust Watermarking Scheme Using Machine Learning Transmitted Over High-Speed Network for Smart Cities. In: Hassanien, A., Elhoseny, M., Ahmed, S., Singh, A. (eds) Security in Smart Cities: Models, Applications, and Challenges. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-01560-2_11

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