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Efficient Image Watermarking Using Particle Swarm Optimization and Convolutional Neural Network

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Advances in Cognitive Science and Communications (ICCCE 2023)

Part of the book series: Cognitive Science and Technology ((CSAT))

Abstract

In today’s Internet environment, copyright protection of digital multimedia material is a major concern. Digital image watermarking provides the concept of secured copyright protection—the strength and quality of the watermark image loss due to geometrical attacks. However, minimization of the impact of geometrical attacks is a major challenge. This paper proposed a coefficient optimization-based watermarking algorithm. The particle swarm optimization applies to feature optimization of the source and symbol images. The processing of feature extraction of the source image and symbol image uses the wavelet transform function. The CNN algorithm follows the process of embedding with an optimal coefficient of the PSO algorithm. The proposed algorithm has been tested on MATLAB environments with reputed image datasets. The performance of the proposed algorithm is estimated as correlation coefficient and PSNR. The estimated results compare CNN algorithm and WCFOA. The improved outcome of the proposed algorithm against the existing algorithm was approx. 2%.

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Authors and Affiliations

  1. RGPV University, Bhopal, India

    Manish Rai, Sachin Goyal & Mahesh Pawar

Authors
  1. Manish Rai
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  2. Sachin Goyal
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  3. Mahesh Pawar
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Corresponding author

Correspondence to Manish Rai .

Editor information

Editors and Affiliations

  1. Bioaxis DNA Research Centre Private Ltd., Hyderabad, Telangana, India

    Amit Kumar

  2. Glenwood, NSW, Australia

    Stefan Mozar

  3. Nordakademie University of Applied Sciences, Elmshorn, Schleswig-Holstein, Germany

    Jan Haase

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Rai, M., Goyal, S., Pawar, M. (2023). Efficient Image Watermarking Using Particle Swarm Optimization and Convolutional Neural Network. In: Kumar, A., Mozar, S., Haase, J. (eds) Advances in Cognitive Science and Communications. ICCCE 2023. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-8086-2_14

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  • DOI: https://doi.org/10.1007/978-981-19-8086-2_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8085-5

  • Online ISBN: 978-981-19-8086-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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