SVD-based digital image watermarking scheme

Chang, Chin‐Chen; Tsai, Pei-Hsun; Lin, Chia-Chen

doi:10.1016/j.patrec.2005.01.004

Cited by 327 publications

(173 citation statements)

References 8 publications

Supporting

Mentioning

171

Contrasting

Unclassified

Order By: Relevance

“…Recently, the singular value decomposition (SVD) has been used extensively as an effective technique in digital watermarking [14][15][16][17][18][22][23][24][25][26][27][28][29][30][31][32][33][34]. Most existing SVD based watermarking techniques are applied in images [16][17][18][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…Most existing SVD based watermarking techniques are applied in images [16][17][18][22][23][24][25][26][27][28][29][30][31][32][33][34]. SVD based audio watermarking techniques, also exist but fewer, some of which can be found in [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…The second group SVD based schemes are 'blind' and embedding watermark information by manipulating the coefficients in the SVD decomposed matrices, such as U or S. These schemes are based on some observations proposed in [14][15][16][17][18]. Particularly, a digital image watermarking algorithm presented in [16] is based on two observations.…”

Section: Introductionmentioning

confidence: 99%

“…In [14,15], the proposed audio watermarking algorithm is based on the observation that changing S slightly does not affect the quality of the signal much and that the singular values in S are consistent under common signal processing operations. In [18], an image watermarking algorithm based on tuning the coefficients in U was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with manipulating U, there are three deficiencies of manipulating S [14,15]. The first is that the modification of the largest coefficients in S [14] would cause a greater degradation [18] and it can then be easily detected and/or destroyed as the embedding position is public. The second is that embedding watermark information based on the modification of the less significant coefficients in S risks their loss after compression, which is based on removing less significant singular coefficients in S [19,20].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A robust audio watermarking scheme based on reduced singular value decomposition and distortion removal

2011

View full text Add to dashboard Cite

a b s t r a c tThis paper presents a blind audio watermarking algorithm based on the reduced singular value decomposition (RSVD). A new observation on one of the resulting unitary matrices is uncovered. The proposed scheme manipulates coefficients based on this observation in order to embed watermark bits. To preserve audio fidelity a thresholdbased distortion control technique is applied and this is further supplemented by distortion suppression utilizing psychoacoustic principles. Test results on real music signals show that this watermarking scheme is in the range of imperceptibility for human hearing, is accurate and also robust against MP3 compression at various bit rates as well as other selected attacks. The data payload is comparatively high compared to existing audio watermarking schemes.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A robust audio watermarking scheme based on reduced singular value decomposition and distortion removal

2011

View full text Add to dashboard Cite

show abstract

SPIHT‐based multiple image watermarking in NSCT domain

Kumar

Singh

Kumar

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

Image watermarking in wavelet domain is a challenging problem as it includes a proper selection of wavelet transforms as well as sub-bands for accurate embedding and recovery of watermarks.However, selection of a suitable wavelet transform for multiple image watermarking is an area of great interest. Therefore, in this work, a multiple watermarking technique using combination of redundant discrete wavelet transforms (RDWT), non-subsampled contourlet transform (NSCT), set partitioning in hierarchical tree (SPIHT), and singular value decomposition (SVD) is proposed. The proposed scheme is aimed to achieve high capacity, robustness, and imperceptibility. The security aspect of the proposed method is enhanced by using Arnold transform. Since RDWT and NSCT are both shift invariant in nature, therefore they are suitable for multiple image watermarking. Furthermore, the proposed method provides flexibility in selection of appropriate sub-bands for watermark embedding and recovery. Experimental results and analysis reveals that the proposed technique gives maximum PSNR, NC, and SSIM value up to 40.97 dB, 1, and 0.9994, respectively. Furthermore, the performance estimation of our technique is found superior to previously reported techniques under consideration. KEYWORDSArnold transform, non-sub sampled contourlet transform, RDWT, SPIHT, SVD INTRODUCTIONRecently, secure transmission of digital data over open/public networks is one of potential challenges. 1 Therefore, there is a great requirement to secure the data transmission over networks. Digital image watermarking is one of promising method for protecting digital data and is widely used for copyright protection and secure communication. 2-4 Image watermarking provides secure transmission of digital data by embedding it into the cover image for content authentications, copyright protection, 5 etc.Discrete wavelet transform (DWT) has been widely used for image watermarking. 6 However, the major drawbacks associated with the DWT are its shift sensitivity and poor directionality that can be avoided by using redundant wavelet transforms. 7 The existing literatures over image watermarking clearly states that the redundant wavelet transforms are suitable for single and multiple images watermarking 7-10 as these transforms do not use down sampling operation in implementation. Thus, no data/information loss occurs during forward and inverse transformation. Furthermore, redundant discrete wavelet transform (RDWT) 11 and non-subsampled contourlet transform (NSCT) 12 are shift invariant. This property enables us to have better reconstruction of images and found useful in many image processing applications. Furthermore, NSCT provides rich directional information, which can be used in embedding single as well as multiple watermarks after forward transformation. 12 It is evident that the performance of combined transform domain techniques (two or more than two) is better than individual one. 7 Thus, being motivated by above facts, an image watermarking technique in NSCT domain using...

show abstract

A blind color image watermarking algorithm combined spatial domain and SVD

Zhang

Wang

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

It has great significance for protecting the image copyright with color image digital watermarking technique, which needs the watermarking algorithm has high robustness, good imperceptibility and short running-time. A blind colour image watermarking algorithm combined spatial domain and singular value decomposition (SVD) is designed to protect the rightful ownership of color image in this paper. The theoretical principle combined SVD and spatial domain is first discussed and proposed in this paper. Based on the derived principle, the watermark information can be directly embedded into the maximum singular value (MSV) of the host image in the spatial domain by the new quantization index modulation with variable quantization step, and the embedded watermark information can be directly extracted from the MSV of the watermarked image block in the spatial domain with geometrical correction. The contributions of this paper include the following four points: (1) the relationship between the changed MSV of SVD and the average changed value of image pixel in the spatial domain is the first derived in this paper; (2) the presented method has the merits of both spatial-domain technique and transform-domain technique; (3) the relationships of each component of the color image are used to improve the invisibility and the geometrical correction of watermarked image is also adapted to further improve the robustness; and (4) this presented method overcomes the

show abstract

SVD-based digital image watermarking scheme

Cited by 327 publications

References 8 publications

A robust audio watermarking scheme based on reduced singular value decomposition and distortion removal

A robust audio watermarking scheme based on reduced singular value decomposition and distortion removal

SPIHT‐based multiple image watermarking in NSCT domain

A blind color image watermarking algorithm combined spatial domain and SVD

Contact Info

Product

Resources

About