Publication detail

General Regression Neural Network Based Audio Watermarking Algorithm Using Torus Automorphism

KAUR, A. DUTTA, M. PŘINOSIL, J.

Original Title

General Regression Neural Network Based Audio Watermarking Algorithm Using Torus Automorphism

English Title

General Regression Neural Network Based Audio Watermarking Algorithm Using Torus Automorphism

Type

conference paper

Language

en

Original Abstract

Accurate extraction of embedded data at the receiver end is still a major point of consideration in audio watermarking area. This paper portrays a blind audio watermarking scheme in transform domain using the combination of properties of audio signal extracted through singular value decomposition and general regression neural network leading to exact extraction of watermark. The security of embedded watermark is assured by using torus automorphism at the embedded side. Results from the experimental setup validate the accuracy of proposed scheme. The payload capacity of proposed algorithm is 62.5 bps. The comparison of proposed scheme with existing ones indicate that the proposed scheme has shown good efficiency in terms of robustness, payload and transparency.

English abstract

Accurate extraction of embedded data at the receiver end is still a major point of consideration in audio watermarking area. This paper portrays a blind audio watermarking scheme in transform domain using the combination of properties of audio signal extracted through singular value decomposition and general regression neural network leading to exact extraction of watermark. The security of embedded watermark is assured by using torus automorphism at the embedded side. Results from the experimental setup validate the accuracy of proposed scheme. The payload capacity of proposed algorithm is 62.5 bps. The comparison of proposed scheme with existing ones indicate that the proposed scheme has shown good efficiency in terms of robustness, payload and transparency.

Keywords

Audio Watermarking, Blindgeneral regression neural network, Singular Value Decomposition, torus automorphism

Released

04.07.2018

Publisher

IEEE

Location

Athens, Greece

ISBN

978-1-5386-4695-3

Book

Proceedings of the IEEE 2018 41st International Conference on Telecommunications and Signal Processing (TSP2018)

Pages from

1

Pages to

4

Pages count

4

BibTex


@inproceedings{BUT150967,
  author="Arashdeep {Kaur} and Malay Kishore {Dutta} and Jiří {Přinosil}",
  title="General Regression Neural Network Based Audio Watermarking Algorithm Using Torus Automorphism",
  annote="Accurate extraction of embedded data at the receiver end is still a major point of consideration in audio watermarking area. This paper portrays a blind audio watermarking scheme in transform domain using the combination of properties of audio signal extracted through singular value decomposition and general regression neural network leading to exact extraction of watermark. The security of embedded watermark is assured by using torus automorphism at the embedded side. Results from the experimental setup validate the accuracy of proposed scheme. The payload capacity of proposed algorithm is 62.5 bps. The comparison of proposed scheme with existing ones indicate that the proposed scheme has shown good efficiency in terms of robustness, payload and transparency.",
  address="IEEE",
  booktitle="Proceedings of the IEEE 2018 41st International Conference on Telecommunications and Signal Processing (TSP2018)",
  chapter="150967",
  doi="10.1109/TSP.2018.8441174",
  howpublished="online",
  institution="IEEE",
  year="2018",
  month="july",
  pages="1--4",
  publisher="IEEE",
  type="conference paper"
}