Morphological wavelet domain watermarking

BHOWMIK, Deepayan and ABHAYARATNE, G. C. K. (2007). Morphological wavelet domain watermarking. In: 2007 15th European Signal Processing Conference. IEEE, 2539-2543.

Full text not available from this repository.
Official URL: http://ieeexplore.ieee.org/document/7099266/?arnum...
Related URLs:

Abstract

Current digital watermarking methods based on the discrete wavelet transform use orthogonal wavelet kernels. In this paper, we discuss the use of morphological wavelets, which are a class of non-linear wavelets, in digital watermarking for scalable coded images. Three different scenarios for embedding the watermark, namely, 1) embedding only in the low pass subband (low-low), 2) embedding only in the high pass subbands (low-high, high-low and high-high) and 3) embedding in all subbands are considered to model popular wavelet domain watermarking methods. The performance of morphological Haar and higher length median wavelets in terms of embedding and detection under content adaptation attacks, such as resolution and quality scalable decoding are shown. Morphological wavelets based watermarking shows a high robustness against the content adaptation attacks, especially in resolution scalability, compared to the conventional orthogonal wavelets based watermarking.

Item Type: Book Section
Additional Information: Originally presented as a poster at the 15th European Signal Processing Conference, Poznan, Poland, 3-7 September 2007, EURASIP. INSPEC Accession Number: 15109152
Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
Page Range: 2539-2543
Depositing User: Deepayan Bhowmik
Date Deposited: 20 Dec 2016 14:10
Last Modified: 18 Mar 2021 22:15
URI: https://shura.shu.ac.uk/id/eprint/14083

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics