Multipurpose image watermarking in the domain of DWT based on SVD and ABC

Ansari, Irshad Ahmad; Pant, Millie

doi:10.1016/j.patrec.2016.12.010

Cited by 108 publications

(76 citation statements)

References 24 publications

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“…The quaternion discrete Fourier transform used to get the maximum central region of the image and the watermark is embedded using magnitudes of low-frequency information [6]. SVD is used to embed the watermark into singular values of the obtained matrix form cover image [12]. Even the hybrid techniques like least significant bit plane and most significant bit planes are used in connection with DWT, SVD ABC to achieve good robustness and user specific threshold imperceptibility [10][11] [12].…”

Section: Related Workmentioning

confidence: 99%

“…SVD is used to embed the watermark into singular values of the obtained matrix form cover image [12]. Even the hybrid techniques like least significant bit plane and most significant bit planes are used in connection with DWT, SVD ABC to achieve good robustness and user specific threshold imperceptibility [10][11] [12]. A non-blind approach based on DWT-SVD digital watermarking using Alpha Blending and Arnold transformation in YCbCr Color space is robust on geometric attacks [20].…”

Section: Related Workmentioning

confidence: 99%

“…The blind digital image watermarking technique uses discrete wavelet transformation (DWT) to locate the suitable area to embed watermark with a predefined block of binary digit sequences. And this image once again embedded into another image using non-blind watermarking technique using DWT and singular value decomposition (SVD) [12] [18]. During the retrieval process, first nonblind technique and then blind technique are used.…”

Section: Related Workmentioning

confidence: 99%

“…The singular value decomposition [12][18] of a m × n real or complex matrix A is a factorization of the form…”

Section: A3 Singular Value Decomposition (Svd)mentioning

confidence: 99%

“…The m columns of U and the n columns of V are called the left singular vectors and right singular vectors of A, respectively. In singular value decomposition of an image, each singular value represents the luminance of image layer and the corresponding pair of the singular vector represents the geometry of the image layer [12] [18]. The singular values in a digital image are less affected by general image processing operations because the bigger singular values preserve most energy of an image and resist against attacks.…”

Section: E U and V Are Orthogonal)mentioning

confidence: 99%

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Serial Digital Color Image Watermarking using Composite Scheme

Savakar¹,

Ghuli²

2017

IJIGSP

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Abstract-Digital watermarking is one of the ways to have Copyright protection for digital information. The digital watermarking scheme used for watermark embedding has to satisfy robustness property to ensure the security of the secret information hidden. The scheme presented here will support the above said statement significantly. We propose here the scheme as composition of both blind and non-blind digital watermarking technique in a process of serial watermarking. A secret binary image is embedded in the first cover image to get first watermarked image by using blind watermarking technique. Then this first watermarked image is again embedded into second cover image to get serial watermarked image using non-blind watermarking technique. To extract secret binary image, first non-blind watermark extraction technique and then blind watermark extraction techniques are used. From this composite approach and serial watermark embedding procedure, we achieved considerable fidelity and robustness against -Rotation, JPEG compression and for noises Salt & pepper, Gaussian, Speckle, Poisson and multiple noises.Index Terms-Composite scheme, Serial watermarking, First cover image, Second cover image, Serial watermarked image, Blind, Type-II Non-blind.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The singular value decomposition [12][18] of a m × n real or complex matrix A is a factorization of the form…”

Section: A3 Singular Value Decomposition (Svd)mentioning

confidence: 99%

Section: E U and V Are Orthogonal)mentioning

confidence: 99%

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Serial Digital Color Image Watermarking using Composite Scheme

Savakar¹,

Ghuli²

2017

IJIGSP

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A Secured and Robust Medical Image Watermarking Approach for Protecting Integrity of Medical Images

Khare

Srivastava

2020

Trans Emerging Tel Tech

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In this paper, a novel medical image watermarking (MIW) technique for tele ‐ medicine applications is proposed. In this approach homomorphic transform (HT), redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) transforms are successfully utilized for developing an effective watermarking technique. Reflectance component of medical host image is obtained using HT, on which RDWT decomposition and SVD are applied. Similarly medical watermark image is processed with RDWT and SVD. Singular values (Svs) were used for embedding the watermark image into host image. 2‐D chaotic Arnold transform (AT) is used for providing extra security to the watermark image by encrypting the watermarked image. Moreover, proposed technique is also tested for several wavelet families. Robustness and imperceptibility of the proposed technique is enhanced as depicted in experimental results under various attacks. Furthermore, better robustness is attained from proposed technique on comparing it with other formerly reported schemes.

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