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Published in: Neural Computing and Applications 5/2020

13-08-2018 | Original Article

SVM-based robust image watermarking technique in LWT domain using different sub-bands

Authors: Mohiul Islam, Amarjit Roy, Rabul Hussain Laskar

Published in: Neural Computing and Applications | Issue 5/2020

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Abstract

In this paper, a robust image watermarking system in lifting wavelet transform domain using different sub-bands has been proposed. SVM classifier is used during watermark extraction to obtain improved robustness under diverse attack conditions. In this work, a detailed analysis of imperceptibility and robustness performance with the use of different sub-bands has been presented. The performance on different sub-band has been analyzed so as to maximize the robustness against different attacks keeping imperceptibility at adequate level. Robustness is observed against various attacks such as noising attacks, denoising attacks, image processing attacks, lossy compression attacks and geometric attacks. It is seen that high-frequency sub-band provides better invisibility, whereas variation of robustness performance on different sub-bands depend on the type of attacks. It is observed from the performance analysis that all the attacks do not have exactly same effect on the frequency content of the image. For instance, noising attack affects every frequency component of the image almost equally, whereas the embedding in high-frequency band makes the system fragile to lossy compression attack. The algorithm is tested on a large image database to observe the variation in the performance of the system. Comparative analysis suggests that the proposed sub-band provides improved performance over some benchmark methods in most of the cases.

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Metadata
Title
SVM-based robust image watermarking technique in LWT domain using different sub-bands
Authors
Mohiul Islam
Amarjit Roy
Rabul Hussain Laskar
Publication date
13-08-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 5/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3647-2

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