Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection

Yunpeng Zhang, Chengyou Wang, Xiaoli Wang and Min Wang
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Yunpeng Zhang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Chengyou Wang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Xiaoli Wang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Min Wang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China

Future Internet, 2017, vol. 9, issue 2, 1-15

Abstract: Watermarking techniques can be applied in digital images to maintain the authenticity and integrity for copyright protection. In this paper, scale-invariant feature transform (SIFT) is combined with local digital watermarking and a digital watermarking algorithm based on SIFT, singular value decomposition (SVD), and all phase biorthogonal transform (APBT) is proposed. It describes the generation process of the SIFT algorithm in detail and obtains a series of scale-invariant feature points. A large amount of candidate feature points are selected to obtain the neighborhood which can be used to embed the watermark. For these selected feature points, block-based APBT is carried out on their neighborhoods. Moreover, a coefficients matrix of certain APBT coefficients is generated for SVD to embed the encrypted watermark. Experimental results demonstrate that the proposed watermarking algorithm has stronger robustness than some previous schemes. In addition, APBT-based digital watermarking algorithm has good imperceptibility and is more robust to different combinations of attacks, which can be applied for the purpose of copyright protection.

Keywords: image watermarking; scale-invariant feature transform (SIFT); all phase biorthogonal transform (APBT); singular value decomposition (SVD); robustness; copyright protection (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2017
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