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A Novel Watermarking Method using Hadamard Matrix Quantization

Journal of ICT Research and Applications (JICTRA) · Vol. 14 No. 1 (2020) · pp. 1–15

DOI
10.5614/itbj.ict.res.appl.2020.14.1.1
ISSN
2338-5499, 2337-5787
Published
2020-07-28
Publisher
DRI - ITB
Language
eng

Abstract

One of the most used watermarking algorithms is Singular Value Decomposition (SVD), which has a balanced level of imperceptibility and robustness. However, SVD uses a singular matrix for embedding and two orthogonal matrices for reconstruction, which is inefficient. In this paper, a Hadamard matrix is used to get a singular matrix for the reconstruction process. Moreover, SVD works with a floating-point value, which takes long processing time, while the Hadamard matrix works with an integer range, which is more efficient. Visual measurement showed that SVD and the new method had average NC values of 0.8321 and 0.8293, whereas the average SSIM values resulted in the same value (0.9925). In terms of processing time, the proposed method ran faster than SVD with an embedding and extraction time of 0.6308 and 0.2163 seconds against 0.8419 and 0.2935 seconds. The proposed method successfully reduced the running time while maintaining imperceptibility and robustness.

Keywords

Collaboration network of the authors

3 authors · 2 co-authorship links

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  • Universitas Dian Nuswantoro
  • Different organisations
  • Same organisation
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Co-authorOrganisationJoint articles
Farah Zakiyah RahmantiUniversitas Dian Nuswantoro1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A Novel Watermarking Method using Hadamard Matrix Quantization</dc:title>
	<dc:creator>Adi, Prajanto Wahyu</dc:creator>
	<dc:creator>Arsiwi, Pramudi</dc:creator>
	<dc:subject xml:lang="en-US">Hadamard matrix</dc:subject>
	<dc:subject xml:lang="en-US">image processing</dc:subject>
	<dc:subject xml:lang="en-US">quantization</dc:subject>
	<dc:subject xml:lang="en-US">SVD</dc:subject>
	<dc:subject xml:lang="en-US">watermarking</dc:subject>
	<dc:description xml:lang="en-US">One of the most used watermarking algorithms is Singular Value Decomposition (SVD), which has a balanced level of imperceptibility and robustness. However, SVD uses a singular matrix for embedding and two orthogonal matrices for reconstruction, which is inefficient. In this paper, a Hadamard matrix is used to get a singular matrix for the reconstruction process. Moreover, SVD works with a floating-point value, which takes long processing time, while the Hadamard matrix works with an integer range, which is more efficient. Visual measurement showed that SVD and the new method had average NC values of 0.8321 and 0.8293, whereas the average SSIM values resulted in the same value (0.9925). In terms of processing time, the proposed method ran faster than SVD with an embedding and extraction time of 0.6308 and 0.2163 seconds against 0.8419 and 0.2935 seconds. The proposed method successfully reduced the running time while maintaining imperceptibility and robustness.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2020-07-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/10546</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2020.14.1.1</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 14 No. 1 (2020); 1-15</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2020.14.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/10546/4856</dc:relation>
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