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A robust security framework for 3D images

A robust security framework for 3D images
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  REGULAR PAPERGaurav Bhatnagar  • Jonathan Wu  • Balasubramanian Raman A robust security framework for 3D images Received: 12 May 2010/Accepted: 22 October 2010/Published online: 9 December 2010   The Visualization Society of Japan 2010 Abstract  Three-dimensional (3D) visualization of spatial and non-spatial data is a well-established practicehaving numerous applications. The cheapest and the most efficient way to 3D visualization is 3D images/ Anaglyphs. 3D images contain 3D information of the objects present in the image. These images are easilyobtained by superimposing left and right eye images in different color in a single image. In this paper, anovel security framework, viz., watermarking scheme, is presented to ensure their security. The proposedsecurity framework is employed in fractional Fourier transform domain of secret color channel followed bythe embedding using singular value decomposition. The secret channels (SEC) are obtained by applyingreversible integer transform on the RGB channels. The experimental results prove the robustness andimperceptibility of the proposed watermarking scheme. Keywords  3D visualization    3D images/Anaglyph    Digital watermarking    Reversible integer transform   Fractional Fourier transform    Singular value decomposition 1 Introduction In the last decades, 3D reconstruction (Trucco and Verri 1998), which is the process of capturing the shapeand appearance of real objects, is of main attraction among computer vision researchers. This process isachieved by either active or passive methods. Active methods are those methods which actively interferewith the reconstructed object, either mechanically or radiometrically. A simple example of a mechanicalmethod is using a depth gauge to measure a distance to a rotating object put on a turntable. On the contrary,passive methods do not interfere with the reconstructed object, they only use a sensor to measure theradiance reflected or emitted by the object’s surface to derive its 3D structure. Typically, the sensor is animage sensor in a camera sensitive to visible light and the input to the method is a set of digital images orvideo.Recently, researchers have come up with a new terminology namely stereogram (Pinker 1997). Astereogram is an optical illusion of depth created from flat, two-dimensional image or images. The mostrenowned type of stereogram is Anaglyph/3D images (Rollmann 1853; Stoffer et al. 2003), which usually G. Bhatnagar ( & )    J. WuUniversity of Windsor, Windsor, ON N9B 3P4, CanadaE-mail: goravdma@gmail.comJ. Wue-mail: jwu@uwindsor.caB. RamanIndian Institute of Technology Roorkee, Roorkee 247667, Indiae-mail: balarfma@iitr.ernet.inJ Vis (2011) 14:85–93DOI 10.1007/s12650-010-0067-5  provides a stereoscopic 3D effect, when viewed with two color glasses (with lenses of chromaticallyopposite color, usually red and cyan). These two words, 3D images and Anaglyphs, are used as synonymousthrough out the paper. 3D images are a recent revitalization due to the presentation of images and videos onthe Internet, Blu-ray HD discs, CDs and even in print. 3D images are made up of two color layers,superimposed, but offset with respect to each other to produce a depth effect. Generally, the main content isin the center, while the foreground and background are shifted laterally in opposite directions. The 3D imagecontains two differently filtered color images, one for each eye. When viewed through the ‘‘color coded‘‘‘‘anaglyph glasses’’, they reveal an integrated stereoscopic image. The visual cortex of the brain fuses thisinto perception of a three-dimensional scene or composition. 3D images are much easier to view than eitherparallel (diverging) or crossed-view pair stereograms. However, these side-by-side types offer bright andaccurate color rendering which is not easily achieved.In recent years, three-dimensional visualization has gained significant interest of research communityin the area of visualization due to its application in the field of scientific arts, such as sculpture,rhythmical movement, fine arts, education and so on. The stressed motive of the present work is to givesecurity to the 3D images. It is apparent that the amount of multimedia data such as photographs,paintings, speech, music, video, documents, etc., is distributed through international communication andmobile networks on a large scale which srcinates the concept of intelligent systems and technology tounderstand, index, manage, search and consume these data. Further, in such an environment, the multi-media data can be copied easily, tampered and transmitted back to the network. As a result, there is astrong need of developing some robust frameworks which ensure the security and authentication of multimedia data. Digital watermarking (Cox et al. 2001; Liu and Tan 2002; Alattar 2003; Ganic and Eskicioglu 2005; Sverldov et al. 2005; Bhatnagar and Raman 2009) is frequently used as the effective solution for persisting problem nowadays.This paper presents a robust framework for the security and authenticity of 3D images via digitalwatermarking. First, the dependent RGB color channels of an srcinal 3D image are secretly mapped to theindependent color channels (SEC channel) by reversible integer transform (RIT) which is followed by thedecomposition of any or all SEC channel using fractional Fourier transform (FrFT). Now, the largestsingular value of each block is obtained from segmented transformed channel and stacked into an array toform key matrix. The watermark, which gives the security and authenticity to the 3D image, is embedded inthe key matrix and embedding is done by modifying its singular values with the watermark singular values.The main benefit of the proposed scheme is the embedding of watermark robustly in the largest singularvalues of segmented blocks which contain most of the block energy and is less affected by the imageprocessing manipulation. The experimental results demonstrate the robustness and superiority of the pro-posed algorithm.The rest of paper is organized as follows: In Sect. 2, mathematical preliminaries are illustrated followedby the proposed security framework for 3D images in Sect. 3. In Sect. 4, experimental results using pro- posed framework are presented and finally Sect. 5 gives the concluding remarks regarding proposedsecurity framework. 2 Mathematical preliminaries This section reviews the basic mathematical concepts and results which are used in the proposed water-marking scheme for 3D images. These concepts are as follows.2.1 Fractional Fourier transform (FrFT)The concept of FrFT is introduced by Victor Namias in 1980 (Namias 1980). The FrFT is the generalizationof Fourier transform. The essence of the generalization is to provide a parameter  a  that can be interpreted asa rotation by an angle  a  in the time–frequency plane or decomposition of the signal in terms of chirps.Generally, this parameter is called an angle or transform order associated with FrFT. Mathematically, theFrFT of a one-dimensional function  s ( t  ) is defined as F  a ½ s ð t  Þð  x Þ ¼ Z  11 s ð t  Þ K  a ð t  ;  x Þ d t  ;  ð 1 Þ 86 G. Bhatnagar et al.  where  a  is the transform order and  K  a ð t  ;  x Þ  is the transform kernel given by K  a ð t  ;  x Þ ¼  ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1    i cot  a p   e i t  2 þ  x 22  cot  a  i  xt   csc  a a  6¼  n pd ð t     x Þ ;  a  ¼  2 n pd ð t   þ  x Þ ;  a  ¼  2 n p    p 8<:  ;  ð 2 Þ where  n  is a given integer. The FrFT of a signal exists under the same conditions in which its Fouriertransform exists. The inverse FrFT can be visualized as the FrFT with transform order  - a  (the detailedillustration on the computation of FrFT can be found in Almedia 1994; Ozaktas et al. 2000). The main property of FrFT is that the signal obtained is in purely time domain if transform order ( a ) is 0 and in purelyfrequency domain if transform order ( a ) is  p  /2. Some of the important properties of the FrFT are sum-marized as follows:1.  Identity operator  :  F  0 is the identity operator. The FrFT of order  a  =  0 is the input signal itself. TheFrFT of order  a  ¼  2 p  also acts as the identity operator because it can be viewed as the successiveapplication of the ordinary Fourier transform four times. Mathematically, F  0 ½ s ð t  Þ ¼  F  2 p ½ s ð t  Þ ¼  s ð t  Þ :  ð 3 Þ 2.  Fourier transform operator  :  F  p  /2 is the Fourier transform operator. The FrFT of order  a  =  p  /2 gives theFourier transform of the input signal.3.  Successive applications of FrFT  : Successive applications of FrFT are equivalent to a single transformwhose order is equal to the sum of the individual orders. Mathematically, F  a ð F  b ½ s ð t  ÞÞ ¼  F  a þ b ½ s ð t  Þ :  ð 4 Þ 4.  Inverse : The inverse FrFT to reconstruct the srcinal signal is the FrFT of order  - a , i.e. F   a ð F  a ½ s ð t  ÞÞ ¼  F   a þ a ½ s ð t  Þ ¼  F  0 ½ s ð t  Þ ¼  s ð t  Þ :  ð 5 Þ 5.  Higher dimensional FrFT  : Due to the separability of the transform, the higher dimensional FrFT can beobtained by successively taking one-dimensional FrFT along all the directions. For instance, 2D FrFTcan be viewed as F  a  x ; a  y ½ s ð t   x ; t   y Þð u ; v Þ ¼  FrFT t   y ! v a  y f FrFT t   x ! u a  x f s ð t   x ; t   y Þgg :  ð 6 Þ The main benefit of using FrFT in proposed security framework via watermarking is its dependence onthe transform orders. These transform orders are used as the keys to the watermark extraction becausewithout using correct transform orders one cannot obtain correct transformed domain in which thewatermark is embedded. Hence, one cannot extract the watermark efficiently with wrong transformorders.2.2 Singular value decomposition (SVD)Let  A  be a general real(complex) matrix of order  m  9  n . The SVD (Strang 1993) of   A  is the factorization  A  ¼  U     S     V  T ð 7 Þ where  U   and  V   are  orthogonal ( unitary ) and  S   ¼  diag ð r 1 ; r 2 ;  . . . ; r r  Þ ;  where  r i ;  i  =  1(1) r   are the singularvalues of the matrix  A  with  r   =  min( m ,  n ) and satisfying  r 1   r 2     r r  :  The first  r   columns of   V   are the right singular vectors  and the first  r   columns of   U   are the  left singular vectors .The use of SVD in digital image processing has some advantages. First, the size of the matrices fromSVD transformation is not fixed. It can be a square or rectangular. Secondly, singular values containintrinsic algebraic image properties. Finally, singular values in a digital image are less affected if generalimage processing is performed. These properties of SVD make it a perfect tool for watermarking. 3 Proposed security framework In this section, some of the motivating factors in design of security framework are discussed. We haveused FrFT and SVD for developing the watermarking scheme. Let us consider  F   is the host 3D image and A robust security framework 87  W   is the watermark. In order to form 3D image, the linear projection method is used which is proposed inDubois (2001). The watermark   W   is embedded in the host 3D image, which can be further extracted forvariety of purposes including identification and authentication. The host 3D image is a color image of size  M   9  N   whereas the watermark image is a gray scale meaningful image/logo instead of Gaussian noisetype sequence and of size  m  9  n . The proposed security framework comprises of two processes, viz. (1)embedding process and (2) extraction process. The first process essentially embeds the watermark in thehost 3D image to get watermarked 3D image, whereas extraction process extracts an estimate of thewatermark from the possibly attacked watermarked 3D image whenever needed. These processes are asfollows.3.1 Secret color channel conversionIn image processing, the integer color transform is a reversible operation that can transform one colorcoordinate into another one and both the inputs and the outputs are of integer forms. There are manyinteger color transforms in literature which are frequently used and are reversible. Since in the presentwork, the main concern is the security of 3D image via watermarking and it is evident that the R, Gand B channels are highly dependent on each other but for a good watermarking scheme this relationmust be broken before embedding so that the watermark will survive a variety of attacks. Therefore, togive randomness and enhance security, RGB color channels are first transformed into secret colorchannels, say SEC. For this purpose, the corresponding pixels are collected from each R, G and Bchannel and transformed by triplet-based reversible integer transform (T-RIT) to get the correspondingpixels of S, E and C channels. Hence, RGB channels are first transformed into three secret independentchannels, i.e. SEC channel using T-RIT and then the embedding is done either in all or any of S, E andC channel.The RIT is a reversible transform which maps integers to integers. The main advantage of RIT isthat it can be implemented by fixed-point processors and no floating-point processor is required. Sincethe computation time for the fixed-point processors is much less, the RIT is usually very efficient. Letus consider a vector triplet  u  ¼ ð u 1 ; u 2 ; u 3 Þ ;  where  u i s are integers. The T-RIT (Alattar 2003) of   u  isgiven by v 1  ¼  a 1 u 1  þ  a 2 u 2  þ  a 3 u 3 a 1  þ  a 2  þ  a 3   ;  v 2  ¼  u 2    u 1 ;  v 3  ¼  u 3    u 1 ;  ð 8 Þ where  b  r   c  is the largest integer not greater than  r   and  a  ¼ ð a 1 ; a 2 ; a 3 Þ  is a constant vector which is secretand plays a vital role of key in the transformation. Obviously, for different  a ;  different values of   v  areobtained. Now, in order to get srcinal integers back from transformed integers ( v ), the inverse T-RIT isdefined as u 1  ¼  v 1    a 2 v 2 a 1  þ  a 2  þ  a 3   a 3 v 3 a 1  þ  a 2  þ  a 3   ; u 2  ¼  v 1  þ ð a 1  þ  a 3 Þ v 2 a 1  þ  a 2  þ  a 3   a 3 v 3 a 1  þ  a 2  þ  a 3   ; u 3  ¼  v 1    a 2 v 2 a 1  þ  a 2  þ  a 3 þ ð a 1  þ  a 2 Þ v 3 a 1  þ  a 2  þ  a 3   ; ð 9 Þ where  d r  e  is the smallest integer not less than  r  . For example, if we want to convert RGB channel values[200 175 145] in the corresponding values in SEC channel with  a  ¼ ½ 171210  ;  the whole process of conversion can be done by Eq. 8 as S   ¼  17    200  þ  12    175  þ  10    14517  þ  12  þ  10    ¼ b 178 : 2051 c ¼  178 ; E   ¼  175    200  ¼  25 ; C   ¼  145    200  ¼  55and reverse process is done by Eq. 9 as 88 G. Bhatnagar et al.   R  ¼  178    12   ð 25 Þ 17  þ  12  þ  10    10   ð 55 Þ 17  þ  12  þ  10    ¼ d 199 : 7949 e ¼  200 ; G  ¼  178  þ ð 17  þ  10 Þ  ð 25 Þ 17  þ  12  þ  10    10   ð 55 Þ 17  þ  12  þ  10    ¼ d 174 : 7949 e ¼  175 ;  B  ¼  178    12   ð 25 Þ 17  þ  12  þ  10  þ ð 17  þ  12 Þ  ð 55 Þ 17  þ  12  þ  10    ¼ d 144 : 7949 e ¼  145 : 3.2 Embedding processThe embedding process is given as follows. •  Map RGB color channels of host 3D image ( F  ) into secret SEC color channels. •  Perform ( a  x ; a  y )-FrFT on selected channel (say  e S  ; ) which is denoted by  f  , where  a  x  and  a  y  are thetransform orders along  x - and  y -axis. •  Segment  f   into non-overlapping blocks of size  p 1    p 2 ;  which are denoted by  f  q ;  where  p 1  ¼ b  M m c ;  p 2  ¼ b  N n c  and  q  =  mn  are the total number of blocks. •  Perform SVD transform on all non-overlapping blocks  f  q , i.e.  f  q ¼  U   f  q S   f  q V  T  f  q :  ð 10 Þ •  Collect the highest singular value of all non-overlapping blocks  f  q and stack into an array of size m  9  n  to form a key matrix ( K  ) as K   ¼ r 1  r 2  r 3  . . .  r n r n þ 1  r n þ 2  r n þ 3  . . .  r 2 n ............... r m ð n þ 1 Þ  r m ð n þ 2 Þ  r m ð n þ 3 Þ  . . .  r mn 2666437775 :  ð 11 Þ •  Perform SVD transform on  K   and watermark image  W K   ¼  U  K  S  K  V  T K  ;  W   ¼  U  W  S  W  V  T W  :  ð 12 Þ •  Modify the singular values of   K   with the singular values of the watermark as S   K   ¼  S  K   þ  b S  W  ;  ð 13 Þ where  b  gives the watermark strength. •  Perform inverse SVD to construct modified  K   as  K  new  ¼  U  K  S   K  V  T K  : •  Map modified highest singular value on their srcinal position followed by inverse SVD to get modifiednon-overlapping blocks  f  q new : •  Map modified blocks to their srcinal position followed by inverse ( a  x ; a  y )-FrFT to get watermarkedchannel  e S  : •  Map modified secret color channel  e SEC to modified RGB channel to get watermarked 3D image  e F  : 3.3 Extraction process •  Map RGB color channel of watermarked 3D image ( e F  ) into secret SEC color channels. •  Perform ( a  x ; a  y )-FrFT on the watermarked channel ( e S  ), which is denoted by  e  f  ;  where  a  x  and  a  y  are thetransform orders along  x - and  y -axis. •  Segment  e  f   into non-overlapping blocks of size  p 1    p 2 ;  which are denoted by  e  f  q ;  where  p 1  ¼ b  M m c ;  p 2  ¼b  N n c  and  q  =  m n  are the total number of blocks. •  Perform SVD transform on all non-overlapping blocks  e  f  q , i.e. e  f  q ¼  U  e  f   q S  e  f   q V  T e  f   q :  ð 14 Þ •  Collect the highest singular value of all non-overlapping blocks  e  f  q and stack into an array of size m  9  n  to form a watermarked key matrix ( e K  ) as A robust security framework 89
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