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A block-based RDWT-SVD image watermarking method using human visual system characteristics

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Ferda Ernawan at Universiti Malaysia Pahang
Ferda Ernawan
Muhammad Nomani Kabir at Universiti Malaysia Pahang
Muhammad Nomani Kabir
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Abstract and Figures

With the rapid growth of internet technology, image watermarking method has become a popular copyright protection method for digital images. In this paper, we propose a watermarking method based on \(4\times 4\) image blocks using redundant wavelet transform with singular value decomposition considering human visual system (HVS) characteristics expressed by entropy values. The blocks which have the lower HVS entropies are selected for embedding the watermark. The watermark is embedded by examining \(U_{2,1}\) and \(U_{3,1}\) components of the orthogonal matrix obtained from singular value decomposition of the redundant wavelet transformed image block where an optimal threshold value based on the trade-off between robustness and imperceptibility is used. In order to provide additional security, a binary watermark is scrambled by Arnold transform before the watermark is embedded into the host image. The proposed scheme is tested under various image processing, compression and geometrical attacks. The test results are compared to other watermarking schemes that use SVD techniques. The experimental results demonstrate that our method can achieve higher imperceptibility and robustness under different types of attacks compared to existing schemes. Our method provides high robustness especially under image processing attacks, JPEG2000 and JPEG XR attacks. It has been observed that the proposed method achieves better performance over the recent existing watermarking schemes.
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The Visual Computer (2020) 36:19–37
https://doi.org/10.1007/s00371-018-1567-x
ORIGINAL ARTICLE
A block-based RDWT-SVD image watermarking method using human
visual system characteristics
Ferda Ernawan1
·Muhammad Nomani Kabir1
Published online: 14 June 2018
© Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract
With the rapid growth of internet technology, image watermarking method has become a popular copyright protection method
for digital images. In this paper, we propose a watermarking method based on 4 ×4 image blocks using redundant wavelet
transform with singular value decomposition considering human visual system (HVS) characteristics expressed by entropy
values. The blocks which have the lower HVS entropies are selected for embedding the watermark. The watermark is
embedded by examining U2,1and U3,1components of the orthogonal matrix obtained from singular value decomposition of
the redundant wavelet transformed image block where an optimal threshold value based on the trade-off between robustness
and imperceptibility is used. In order to provide additional security, a binary watermark is scrambled by Arnold transform
before the watermark is embedded into the host image. The proposed scheme is tested under various image processing,
compression and geometrical attacks. The test results are compared to other watermarking schemes that use SVD techniques.
The experimental results demonstrate that our method can achieve higher imperceptibility and robustness under different types
of attacks compared to existing schemes. Our method provides high robustness especially under image processing attacks,
JPEG2000 and JPEG XR attacks. It has been observed that the proposed method achieves better performance over the recent
existing watermarking schemes.
Keywords Image watermarking ·Arnold transform ·Human visual characteristics ·Redundant wavelet transform ·Singular
value decomposition
1 Introduction
In the present era, people can easily access, save, transmit
and distribute digital data, e.g., image, video, audio, text,
etc., with the advancement of Internet and telecommunica-
tion technologies. They can easily copy, sell and buy without
permission of their owners. Due to this issues, the copy-
right protection becomes challenging to provide inherent
security of the ownership right [1,2]. Digital watermark-
ing is a potential solution for dealing with this issue [3,4].
Watermarking algorithms play a vital role in the quality (no
significant distortion in the watermarked image with respect
to the original one), robustness (ability to withstand various
BFerda Ernawan
ferda@ump.edu.my; ferda1902@gmail.com
1Faculty of Computer Systems and Software Engineering,
Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300
Gambang, Kuantan, Pahang Darul Makmur, Malaysia
image manipulations) and security (inability to detect the
embedded watermark) of watermarked images.
Recently, various watermarking methods have been devel-
oped to achieve quality, robustness and security of the
watermarked images. Several researchers have proposed a
watermarking method in which the watermark is embed-
ded after modification of transform coefficients; and con-
sequently, when the image was inverse-transformed, the
technique distributes the energy of embedded signal over
all pixels in the spatial domain, which can reduce the distor-
tion of the embedded watermarks in the host image. The
mostly used transforms include discrete cosine transform
(DCT) [5], discrete wavelet transform (DWT) [6], redundant
discrete wavelet transform (RDWT), integer wavelet trans-
form (IWT), etc. Moreover, the current research interest isto
combine the mathematical techniques: singular value decom-
position (SVD) and frequency transform such as DCT, DWT
and IWT. [7,8]. One of the major problems of block-based
DCT is the blocking effect/artifact image which appears
along the embedding blocks. To avoid this problem, DWT
123
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... According to SVD [13,14], the hidden information is embedded, incorporating the source's singular value picture data. Shift-invariant directionality is a property of RDWT-based watermarking, which also solves the DWT issue [15][16][17]. Block-based SVD increases robustness and security, and PSO makes the proposed scheme adaptive. ...
... Even for a little shift throughout the entire image, the down sampling method in DWT achieves a shift variant. Shift variants cause both significant changes in the image wavelet coefficients and small shifts in the original image [16,17]. ...
... This is the main difficulty with DWT-based watermarking. The RDWT techniques have been suggested as a solution to the DWT issue since they have directionality and shift-invariant qualities [17,25]. According to Figs. 1 and 2, at the same stage of deterioration, the sub-band's width decreases in DWT but is the same in RDWT as the original image. ...
An Adaptive Block-Based Watermarking Scheme Using RDWT-SVD and Particle Swarm Optimization
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The major goal of digital image watermarking is to acquire security, robustness and reliability against various attacks. To achieve this objective, embedding the watermark into the cover image and selecting the embedding factor is a tedious task. In this paper presents a Redundant discrete wavelet transform (RDWT), block-based singular value decomposition (SVD), and secure and robust image watermarking method using particle swarm optimization (PSO) technique for choosing the scale factor. The presented method first decomposed the cover image using RDWT and then segmented all sub-bands into non-overlapping blocks. SVD is enforced on the LL sub-band and modifies the singular values of these blocks by the watermark image. PSO is used for selecting the scaling factor to increase imperceptibility and robustness. The experimental outcomes show that the presented method performs well as compared to previously introduced techniques, is more imperceptible and highly robust against the various image processing attacks, and also removes the diagonal line problem from the extracted watermark.
View
... The original fingerprint image is X , while the output fingerprint image after perturbation is X  . The process of privacy protection involves adding Laplace noise perturbation to each pixel in the fingerprint image, and the equation (8) can be derived for the protected fingerprint image through the Laplace mechanism. ...
... Algorithm LAP is a method to protect fingerprint images globally. In (8), disturbance noise should be added to all pixel values in the process of fingerprint image protection. Therefore, the global sensitivity in the differential privacy protection process of algorithm LAP is related to the image size. ...
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Due to the inherent privacy concerns associated with fingerprint image, technology of fingerprint recognition has been extensively utilized for identity authentication. However, direct publication of fingerprint image on the Internet can result in the leakage of sensitive information. In order to protect the sensitive information in fingerprint image, this paper proposes a novel method called LSDP that combines wavelet transform and singular value decomposition while ensuring the differential privacy protection. The primary focus of protecting fingerprint image involves safeguarding matrix of two-dimensional image. To mitigate excessive noise generated by the differential privacy when adding noise to the matrix of fingerprint image, this study employs wavelet transform as a solution. Additionally, the exponential mechanism is utilized in conjunction with the wavelet transform to select an appropriate threshold in order to reduce noise added by the differential privacy. This approach is referred to as DWP. Furthermore, the singular value decomposition applied to the wavelet transforms coefficients. In order to reduce the effect of noise errors, perturbation noise is only added to the singular values instead of all coefficients. This proposed method is termed SDP. To enhance the privacy protection of publication of fingerprint image, the proposed LSDP add perturbation noise to only some of the singular values. The experimental results demonstrate that our proposed algorithm outperforms the direct application of the wavelet transform or the singular value decomposition in terms of safeguarding fingerprint image.
View
... One of the important steps is to select the region for watermarking it may be done using segmentation [4] or other method. Discrete wavelet transforms (DWT) [5][6], discrete cosine transforms (DCT) [7][8], discrete fourier transform (DFT) [9][10], redundant discrete wavelet transforms (RDWT) [11][12], and integer/Lifting wavelet transform (IWT/LWT) [13][14][15][16][17][18] have previously been used for image watermarking. LWT is second generation wavelet and it has various advantages over the conventional methods and it is most significantly known for absorbing more image distortion due to having more energy compaction properties. ...
... Looking back at the literature, it is obvious that machine learning-based color image watermarking has emerged as a viable method for balancing PSNR and NC in a fair manner, although creating a robust color image watermarking system is a more difficult task. Watermarking extraction is treated here as a binary classification and various classification techniques like SVM [15], SVD [6,11,16,17,20], 2DLDA [21] and deep network [18,20]. ...
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... Several image watermarking have been reported recently using the transform-domain method as it has shown good robustness and improvement against various image attacks as contrasted to the spatial-domain-based watermarking. Primarily the transform domain-based watermarking has applied discrete wavelet transform (DWT) [5][6][7][8], discrete cosine transform (DCT) [9,10], discrete Fourier transforms (DFT) [11,12], redundant discrete wavelet transforms (RDWT) [13,14], and integer wavelet transform (IWT) [15][16][17][18][19][20][21] for image watermarking. Researchers have spent considerable time on integer or lifting wavelet transform (LWT) based image watermarking because it has various advantages over more conventional transform techniques. ...
... Machine learning [5][6][7][8][9][10][11][12][13][14][15][16][17] and deep learning [18][19][20] based image watermarking method have been developed in the last decade because it has shown good robustness against various image attacks compared to the traditional image watermarking methods. Singular value decomposition (SVD) [5-8, 12, 13, 17, 21] is also applied in image watermarking techniques due to its suitable energy compaction property, and it is mainly used for dealing with false positive rate (FPR) problems in image watermarking. ...
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View
... First, SVD is one of the earliest and most common matrix transformations that has been employed for digital image watermarking schemes. Some studies are notable, such as [4][5][6][7][8][9][10][11]. Image watermarking methods based on SVD often embed information on the first element D(1,1) of the upper triangular matrix D [6,7] or on the first column of the matrix U [5,8,9]. ...
... Some studies are notable, such as [4][5][6][7][8][9][10][11]. Image watermarking methods based on SVD often embed information on the first element D(1,1) of the upper triangular matrix D [6,7] or on the first column of the matrix U [5,8,9]. In 2020, Luo et al. used a scheme that combined the discrete wavelet transform (DWT) and SVD with logistic map (LM) [9]. ...
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For digital image copyright protection, watermarking techniques are a promising solution and are of interest to many researchers. In watermarking schemes based on matrix transformation, the embedding element and embedding formula play a very important role in maintaining the quality of a watermark image and the robustness of the watermark. In this paper, a blind image watermarking scheme based on Hessenberg decomposition, where the improvement focuses on the embedding element and embedding formula, is proposed. First, the structure of the Hessenberg factorization is analysed to obtain the most suitable embedding element. Accordingly, this is the first time that the element on the second row and the second column of the upper Hessenberg matrix is selected as an embedding element in a Hessenberg-based image watermarking scheme because of its energy concentration and stability. Second, an improved embedding formula is proposed to address the limitations of previous studies. In the proposed formula, constraint conditions are added to limit the change in all blocks, and a scaling factor is applied to guarantee a trade-off between invisibility and robustness. Here, the scaling factor is carefully calculated by repeating various experiments under different image attacks to achieve an optimal value. Therefore, our proposed embedding formula not only minimizes the modification of the host image after embedding but also helps maintain the robustness of the extracted watermark. Third, to increase the security of the proposed scheme, the watermark image is encoded by the Arnold transform before it is embedded into the host image. The experimental results show that the proposed approach defeats the compared methods in terms of invisibility and execution time. Moreover, the proposed scheme can resist most common attacks when the average normalized correlation value is higher than 0.93 and the extracted watermarks are always clearly recognized.
View
... For example, a spatial domain color image watermarking method was proposed by Abraham et al. [5], which supports imperceptible watermarking and high resistance to attacks. The frequency domain watermarking methods use mathematical transforms such as redundant discrete wavelet transform (RDWT) [12],the discrete wavelet transform (DWT) [13][14][15][16], discrete cosine transform (DCT) [8,9,17], and discrete Fourier transform (DFT) [18] to transform the carrier image to the frequency domain, and the watermark information is embedded in the frequency coefficients. Compared with the spatial domain method, the frequency domain method has better imperceptibility and robustness. ...
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View
... Transformation techniques achieve great performance regarding imperceptibility and robustness and thus have become a research hot spot in watermarking. Typical transform methods include the discrete wavelet transform (DWT) [22,30], integer wavelet transform (IWT) [51], and discrete cosine transform (DCT) [14,16], among others [12,20]. In addition, the matrix decomposition approaches are also pervasively employed in the transformation, such as Schur decomposition [50,53], QR decomposition [19,52] and the classical and powerful image processing tool, singular value decomposition (SVD) [13,21]. ...
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