SVD-Based Approach to Transparent Embedding Data into Digital Images

Gorodetski, Vladimir I.; Popyack, Leonard J.; Samoilov, V. V.; Skormin, Victor A.

doi:10.1007/3-540-45116-1_26

Cited by 78 publications

(59 citation statements)

References 19 publications

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“…This also explains why the LL subband is chosen for watermark embedding. Gorodetski et al proposed an approach on embedding some data through slight modifications of singular values of a small block of the segmented covers [13]. Chandra divided the image into subblocks, applied SVD to those blocks, and modified their largest singular value by a watermark and a scaling factor [14].…”

Section: Related Workmentioning

confidence: 99%

A new approach of nonblind watermarking methods based on DWT and SVD via LU decomposition

Jane¹,

Elbaşi²

2014

Turk J Elec Eng & Comp Sci

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Multimedia security has been the focal point of considerable research activity in the last decade, mainly because of its wide application area. Watermarking, in particular, is identified as a major technology to achieve copyright protection and multimedia security. Therefore, recent studies in the literature include some evident approaches for embedding data into a multimedia element. Because of its useful frequency component separation, the discrete wavelet transform (DWT) is commonly used in watermarking schemes. Moreover, singular value decomposition (SVD) and lower-and-upper (LU) decomposition have little effect on the perception of the watermark. Therefore, in this study, a combination of DWT and SVD via LU decomposition is proposed as a new nonblind watermarking algorithm that requires cover work to detect the watermark. Experimental results show that the proposed algorithm is considerably robust and reliable against certain attacks without degrading the input image, by embedding a binary watermark on the low-low band. Moreover, the threshold values are data-dependent for watermarks after attacks; that is, the threshold value is always different from another for certain attacks so that similarity ratios in this algorithm, as a quality metric, are much more than those of the other algorithms consisting of DWT and/or SVD despite strong attacks causing lower peak signal-to-noise ratio values. Apart from robustness, reliability, and data-dependence, the other novel aspect of this study is to expand the application areas of watermarking with a new algorithm consisting of DWT, LU, and SVD, and this study will contribute to the literature for certain cases.

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Section: Related Workmentioning

confidence: 99%

A new approach of nonblind watermarking methods based on DWT and SVD via LU decomposition

Jane¹,

Elbaşi²

2014

Turk J Elec Eng & Comp Sci

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“…Singular value decomposition (SVD) is a numerical technique used to diagonalize matrices in numerical analysis [3,4]. SVD is an attractive algebraic transform for image processing, because of its endless advantages, such as maximum energy packing which is usually used in compression [5,6], ability to manipulate the image in base of two distinctive subspaces data and noise subspaces [6,7,8], which is usually uses in noise filtering and also was utilized in watermarking applications [9,6]. Each of these applications exploit key properties of the SVD.…”

Section: Introductionmentioning

confidence: 99%

SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges

Sadek¹

2012

IJACSA

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Abstract-Singular Value Decomposition (SVD) has recently emerged as a new paradigm for processing different types of images. SVD is an attractive algebraic transform for image processing applications. The paper proposes an experimental survey for the SVD as an efficient transform in image processing applications. Despite the well-known fact that SVD offers attractive properties in imaging, the exploring of using its properties in various image applications is currently at its infancy. Since the SVD has many attractive properties have not been utilized, this paper contributes in using these generous properties in newly image applications and gives a highly recommendation for more research challenges. In this paper, the SVD properties for images are experimentally presented to be utilized in developing new SVD-based image processing applications. The paper offers survey on the developed SVD based image applications. The paper also proposes some new contributions that were originated from SVD properties analysis in different image processing. The aim of this paper is to provide a better understanding of the SVD in image processing and identify important various applications and open research directions in this increasingly important area; SVD based image processing in the future research.

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“…Though here the visual of perception of original image is preserved, the watermarked image when subjected to some intentional attacks like compression the watermark bits will get damaged. Coming to the spatial domain watermarking using numerical transformation like SVD (Gorodetski [9], liu et al [10]) they provide good security against tampering and common manipulations for protecting rightful ownership. But these schemes are non adaptive, thus unable to offer consistent perceptual transparency of watermarking of different images [11]To provide adaptive transparency, robustness to the compressions and insensitivity to malicious manipulations, we propose a novel image hybrid watermarking scheme using NSCT and SVD.…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Watermarking Technique Based on Nonsubsampled Contourlet Transform and SVD

Narasimhulu¹,

Prasad²

2011

IJMA

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The paper proposes a novel robust watermarking technique based on newly introduced Nonsubsampled contourlet transform(NSCT) and singular value decomposition(SVD) for multimedia copyright protection. The NSCT can give the asymptotic optimal representation of the edges and contours in image by virtue of the characteristics of good multi resolution shift invariance and multi directionality. After decomposing the host image into sub bands, we choose the low frequency directional sub band and apply singular value decomposition. The singular values of the original image are then modified by the singular values of nonsubsampled contourlet transformed visual grayscale logo watermark image. This hybrid approach improves the performance of the watermarking technique compared to earlier techniques. Experimental results shows that the hybrid technique is resilient to various linear and non linear filtering

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SVD-Based Approach to Transparent Embedding Data into Digital Images

Cited by 78 publications

References 19 publications

A new approach of nonblind watermarking methods based on DWT and SVD via LU decomposition

A new approach of nonblind watermarking methods based on DWT and SVD via LU decomposition

SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges

A Novel Robust Watermarking Technique Based on Nonsubsampled Contourlet Transform and SVD

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