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\title{Three Dimensional Discrete Wavelet Transform Based Robust Multiple Watermarking Algorithm for Spectral Images}
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             \author[1]{Dr.  S.Maheswari}

             \author[2]{  K.Rameshwaran}

             \affil[1]{  Kongu Engineering College}

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\date{\small \em Received: 27 October 2011 Accepted: 22 November 2011 Published: 6 December 2011}

\maketitle


\begin{abstract}
        


Three Dimensional (3-D) imaging is a fast growing technology in the areas of computer  graphics, robotics, metrology, medicine and cultural heritage etc. With the development of internet, 3-D  contents are easily manipulated, transmitted and widely distributed. Watermarking has been proved to be  an efficient technology in copyright protection of multimedia information. In this paper, a novel blind  multiple watermarking algorithm for copyright protection of spectral images is presented. Watermarking is  based on 3-D Discrete Wavelet Transform (DWT). Dual binary watermarks are proposed to embed in the  mid frequency bands of cover image. One level 3-D DWT is applied to the original spectral image and one  level 2-D DWT is applied on two binary watermarks. Eigen values of selected subbands of spectral image  are modified by the eigen values of selected subbands of two binary watermarks. Inverse SVD (Singular  Value Decomposition)  and Inverse 3-D DWT is taken on the modified subbands to obtain the  watermarked image. During extraction process, two binary watermarks are recovered from the test image  with the help of a secret key. An experimental result shows that, the proposed algorithm produces very  high imperceptibility and robustness against various types of attacks like low pass filtering, median  Filtering, JPEG compression, histogram equalization, cropping, various angles of rotation and addition of  noise like Gaussian, Salt &Pepper, Speckle, Poisson etc.  

\end{abstract}


\keywords{DWT, 3-D DWT, SVD and Multiple Watermarking.}

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\let\tabcellsep& 	 	 		 
\section[{INTRODUCTION}]{INTRODUCTION}\par
igital watermarking and steganography techniques are used to address digital rights management, protect information, and conceal secrets. During the last decade, 3-D meshes have been widely used in industrial, medical and entertainment applications. Since 3-D watermarking has also become an active research topic to protect 3-D information, we present a new frame work for copyright protection of 3-D spectral images in this paper. Spectral imaging is a practical tool for various applications like medical imaging, industrial quality control, digital commerce and maintenance of cultural heritage in digital museums etc \hyperref[b0]{[1]}. In spectral color imaging, color of an object can be Author ? : Kongu Engineering College, Perundurai, Erode-638052, Tamil Nadu, India. ( E-mail : maheswari\textunderscore bsb@yahoo.com ).\par
Author ? : MAM Group of Institutions, Triuchirapalli-620009, Tamil Nadu, India. ( E-mail : krameshwaran @gmail.com ).\par
represented more accurately as compared to traditional three channel RGB images.\par
Three conflicting requirements of watermarking systems are robustness, imperceptibility and capacity  {\ref [2]}. Any watermarking system should allow embedding large number of secret information. Embedding of secret message should not degrade the quality of image; it should withstand in the image; for all kinds of attacks like JPEG compression, low pass filtering, median filtering, noise addition, histogram equalization, rotation, scaling, cropping etc. Embedding of multiple watermarks enhances the robustness property. Embedding of multiple watermarks, instead of a single one into one cover image improves the robustness of watermarking scheme \hyperref[b1]{[3]}.\par
Transform domain technique offers very high robustness as compared to spatial domain technique; but it needs more computational complexity because input images are converted into transform coefficients by using various image transforms like DCT \hyperref[b2]{[4]}\hyperref[b3]{[5]}, DFT and DWT \hyperref[b4]{[6]}\hyperref[b5]{[7]}\hyperref[b6]{[8]}\hyperref[b7]{[9]} etc. 3-D watermarking techniques are classified into two types \hyperref[b8]{[10]}, similar to that of 2-D watermarking, spatial domain technique \hyperref[b8]{[10]} and spectral domain technique \hyperref[b2]{[4]}\hyperref[b3]{[5]}\hyperref[b4]{[6]}\hyperref[b5]{[7]}\hyperref[b6]{[8]}\hyperref[b7]{[9]}. Spatial domain techniques are further classified into two types. They are Geometry modification technique and Topology modification technique. In spectral method; information can be embedded in one of the mesh transform domains like mesh spectral decomposition, wavelet transform or spherical transform.\par
The use of wavelets in image and video coding has increased significantly over the years, mainly due to the superior energy compaction property of wavelets compared with the traditional transforms like DCT \hyperref[b9]{[11]}. In this paper, we have proposed a method to embed the watermark in wavelet transform domain. The 3-D object is processed for obtaining 2-D slices, so that each 3-D objects are represented by a set of 2-D slices. A 3-D DWT domain, obtained by performing a 2-D spatial wavelet transform and then a temporal 1-D wavelet transform  {\ref [12]}. The Haar Wavelet Transform consistently outperforms the more complex ones when using noncoloured watermark \hyperref[b10]{[13]}. Therefore, we propose 3-D Haar Wavelet Transform based watermarking scheme. Eigen values of mid frequency subbands of a cover image and two binary watermarks are obtained by 
\section[{( I )}]{( I )}\par
Singular Value Decomposition (SVD) \hyperref[b0]{[1,}\hyperref[b6]{8]}. Eigen values of mid frequency subbands are modified by the Eigen values of dual binary watermarks. Section (2) deals with 3-D DWT. Section (3) discusses about the proposed embedding and extraction algorithm, Section (4) gives the experimental results of proposed scheme, followed by conclusion in section \hyperref[b3]{(5)}.     
\section[{II. THREE DIMENSIONAL DWT}]{II. THREE DIMENSIONAL DWT} 
\section[{PROPOSED METHOD}]{PROPOSED METHOD}\par
In recent years, several 3-D watermarking techniques have been proposed for gray scale and colour images for authentication, copyright protection, finger printing and ownership assertion. Here, we propse a novel blind watermarking scheme based on wavelet transform for spectral images. Designing of blind watermarking i.e. extracting the watermark without original image and original watermark is a very difficult task \hyperref[b7]{[9]}. In this paper, blind watermarking scheme has been employed. Watermark is extracted with the help of a secret key.Mid frequency bands are selected to embed the secret message in order to compromise between imperceptibility and robustness. Watermark embedding and extraction processes are explained in the following sections. ). Normalised Correlation of recovered watermark is calculated on comparing with original watermark. Watermark extraction scheme has been shown in Figure \hyperref[fig_4]{5}. 
\section[{IV. EXPERIMENTAL RESULTS}]{IV. EXPERIMENTAL RESULTS}\par
The experiments were performed on two different hyperspectral natural images \hyperref[b11]{[14]}. Natural image of the size 256x256x30 and two binary watermarks of size 32x32 were taken to evaluate the proposed algorithm. PSNR value of the watermarked image is calculated by using the equations (  {\ref 1}) and (2).\par
(1)\par
(2) where m, n, and l are dimensions of the image and are the pixel values of the original and watermarked image. Normalized Correlation (NC) between original watermark and extracted watermark from the test image is calculated by using the equation (  {\ref 3}).\par
(3) PSNR value of the watermarked image and Normalised Correlation of recovered watermark are shown in Table  {\ref 1}.\par
Table  {\ref 1}: PSNR Value of Watermarked Image and NC Any watermarking system should be robust against various image processing attacks. It should not be removable by unauthorized users and it should not degrade the quality of the images. There are many attacks against which image watermarking system could be judged. The attacks include JPEG compression, Histogram equalization, various filtering operations like, average filtering, median filtering, addition of noise like Salt and Pepper noise, Gaussian noise, speckle noise and Poisson noise, cropping, various angles of rotation and so on. These attacks are applied to the watermarked images to evaluate recovery process. Table \hyperref[tab_0]{2}, 3 and 4 shows the PSNR value and NC under various attacks. Table \hyperref[tab_3]{5} shows the extracted watermark image under various attacks from Natural Image I.   The proposed watermarking scheme is compared with existing recently published papers by Arto Kaarna et al \hyperref[b12]{[15]}, Long Mal et [16],the results are shown in Tables \hyperref[tab_5]{6 and 7}.  
\section[{CONCLUSION}]{CONCLUSION}\par
Three Dimensional mesh watermarking is an interesting and promising research area. In this paper, a novel blind multiple watermarking algorithm based on 3-D DWT for copyright protection of spectral images has been proposed. Dual binary watermarks have been proposed to embed in mid frequency bands to increase the robustness against attacks and to improve imperceptibility. Extracting the secret message without the help of original image and original watermark is a very difficult task. We extracted dual binary watermarks with the help of details of subband selection and strength factor only. Experimental result shows that the proposed scheme achieves very high imperceptibility and robustness against various image processing attacks like LPF, Median Filtering, JPEG Compression, histogram equalization, cropping, various angle of rotation and addition of noise like Gaussian, Salt \& Pepper, Speckle, Poisson etc. \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-2.png}
\caption{\label{fig_0}A 3 -}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{133134}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Fig. 1 : 3 . 3 . 1 / 3 : 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figures 2 and 3}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{23}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Fig. 2 :Fig. 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Fig. 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Figure 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-8.png}
\caption{\label{fig_7}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.2833333333333333\textwidth}P{0.26505376344086023\textwidth}P{0.23763440860215052\textwidth}P{0.00913978494623656\textwidth}P{0.0456989247311828\textwidth}P{0.00913978494623656\textwidth}}
\tabcellsep \tabcellsep \multicolumn{3}{l}{Normalised}\\
\tabcellsep PSNR\tabcellsep \multicolumn{3}{l}{correlation}\\
\tabcellsep Value in dB\tabcellsep W 1\tabcellsep *\tabcellsep W 2\tabcellsep *\\
Natural Image I\tabcellsep 69.7494\tabcellsep 1\tabcellsep \tabcellsep 1\\
Natural Image II\tabcellsep 57.7162\tabcellsep 1\tabcellsep \tabcellsep 1\end{longtable} \par
 
\caption{\label{tab_0}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{}
\end{longtable} \par
  {\small\itshape [Note: 1) under various Attacks.]} 
\caption{\label{tab_1}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.85\textwidth}}
I )\\
Global\end{longtable} \par
  {\small\itshape [Note: 2) under various Attacks.]} 
\caption{\label{tab_2}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.85\textwidth}}
© 201\end{longtable} \par
 
\caption{\label{tab_3}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.5775641025641026\textwidth}P{0.27243589743589747\textwidth}}
Methods\tabcellsep PSNR in dB\\
Arto Kaarna et al\tabcellsep 38.95\\
Long Mal et al\tabcellsep 48.74\\
Proposed method\tabcellsep 69.74\end{longtable} \par
 
\caption{\label{tab_4}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.116793893129771\textwidth}P{0.17519083969465649\textwidth}P{0.4282442748091603\textwidth}P{0.11030534351145037\textwidth}P{0.019465648854961833\textwidth}}
LPF\tabcellsep Arto\tabcellsep Long\tabcellsep \multicolumn{2}{l}{Proposed}\\
\tabcellsep Kaarna et\tabcellsep Mal\tabcellsep \multicolumn{2}{l}{method}\\
\tabcellsep al\tabcellsep et al\tabcellsep W 1\tabcellsep W 2\\
3 * 3\tabcellsep 0.66\tabcellsep \multicolumn{3}{l}{0.73 0.9582 0.9576}\\
5 * 5\tabcellsep 0.43\tabcellsep \multicolumn{3}{l}{0.56 0.9473 0.9477}\\
7 * 7\tabcellsep 0.30\tabcellsep \multicolumn{3}{l}{0.43 0.9429 0.9429}\end{longtable} \par
 
\caption{\label{tab_5}Table 7 :}\end{figure}
 			\footnote{December} 			\footnote{© 2011 Global Journals Inc. (US)} 			\footnote{DecemberThree Dimensional Discrete Wavelet Transform Based Robust Multiple Watermarking Algorithm for Spectral Images} 			\footnote{Global I ) 2011 December ( Three Dimensional Discrete Wavelet Transform Based Robust Multiple Watermarking Algorithm for Spectral Images} 			\footnote{( I ) 2011 December Three Dimensional Discrete Wavelet Transform Based Robust Multiple Watermarking Algorithm for Spectral Images} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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