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Abstract

This paper presents an audio watermarking technique based on singular value decomposition (SVD) and fractional Fourier transform (FRT). The basic idea of this technique is to implement SVD watermarking on the audio signals in the FRT domain due to its recommended degree of security resulting from using a rotation angle in addition to the frequency-domain transformation. The SVD has an invariance to changes in the signal after watermark embedding. Hence, the proposed technique has a large degree of security and resistance to attacks. This technique is based on embedding an image watermark in either the audio signal or a transformed version of this signal. Experimental results show that watermark embedding in the FRT of an audio signal achieves less distortion of the audio signal in the absence of attacks. In the presence of attacks, it is recommended that the embedding is performed in the FRT of the audio signal to maintain a high detection correlation coefficient between the original watermark and the obtained watermark. A segment-based implementation of the proposed audio watermarking technique is also presented. This implementation succeeds in obtaining a high detection correlation coefficient in the presence of severe attacks. It is noticed from the results that in the presence of attacks, the SVD watermarking in the FRT domain with a phase angle of 5π/4 is better for watermark detection than watermarking using other angles in the FRT domain.

Details

Title
Efficient SVD-based audio watermarking technique in FRT domain
Author
Abdelwahab, Khaled M 1 ; Abd El-atty Saied M 1 ; El-Shafai, W 1 ; El-Rabaie, S 1 ; Abd El-Samie F E 1 

 Menoufia University, Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menouf, Egypt (GRID:grid.411775.1) (ISNI:0000 0004 0621 4712) 
Pages
5617-5648
Publication year
2020
Publication date
Mar 2020
Publisher
Springer Nature B.V.
ISSN
13807501
e-ISSN
15737721
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2375389497
Copyright
Multimedia Tools and Applications is a copyright of Springer, (2019). All Rights Reserved.