Blind Biometric Watermarking Based On Contourlet Transform

IEEE-20180

Blind Biometric Watermarking Based On Contourlet Transform Kishore Kumar N K , V.S Sheeba I

L

1M Tech student, Govt. Engineering College Thrissur, [email protected], 2

Projessor, Govt. Engineering College Thrissur, [email protected]

Abstract- we present a blind biometric

watermarking algorithm

based on Contourlet transform in this paper. The host image is decomposed into a series of multiscale, local and directional subimages

using

Contourlet

transform.

A

high

frequency

directional sub-band w hich has the highest energy is used for watermarking embedding . At the same time, the original binary watermark is scrambled by two-dimensional Arnold transform, and then embedded into the selected directional sub-band. The sign of the contourlet coefficients are modified depending on the watermark bit. The watermark retrieving algorithm is a blind detection process, and it does not need the original image. The experimental

results

show

that

the

proposed

watermarking

algorithm is able to resist attacks, and it is invisible and robust.

Keywords- Blind watermarking, Arnold Transform, Contourlet transform ,PSNR. T.

INTRODUCTION

Watennarking is a branch of infonnation hiding which is used to hide proprietary information in digital media like photographs, digital music, or digital video. The ease with which digital content can be exchanged over the Internet has created copyright infringement issues. Copyrighted material can be easily exchanged over peer-to-peer networks, and this has caused major concerns to those content providers who produce these digital contents. In order to protect the interest of the content providers, these digital contents can be watennarked. The process of embedding a watennark in a multimedia object is tenned as watennarking. Watennark can be considered as a kind of a signature that reveals the owner of the multimedia object. The embedding process is guided by the use of a secret key which decides the locations within the multimedia object (image) where the watermark would be embedded. Wavelet transform can be a good representation of one-dimensional signals, however, it can't be applied to two dimensional signals. Natural images contain intrinsic geometrical structures that are key features in visual infonnation. Wavelets in two dimension are good at isolating the discontinuities at edge points, but will not see the smoothness along the contours , it has only limited directions and it cannot effectively capture the contour information. So

wavelets can capture only limited directional infonnation - an important feature of multidimensional signals. In this sense, the wavelet is not an optimal multiresolution expression of an image. In 2002, Minh N Do and Martin Vetterli proposed a two-dimensional transfonn in order to overcome this lim itation of wavelet transform; it can be a good representation of the mathematical tools- Contourlet transform [CT] [1]. In addition to multiscale and time-frequency localization properties of wavelets, CT offers directionality and anisotropy. Its advantage is that it can effectively express a smooth contour which is an important feature in natural images. Zaboli and Moin [2] used the Human Visual System characteristics and an entropy-based approach to create an efficient watermarking scheme. It decomposes the original image in CT domain in four hierarchical levels and watermarks it with a binary logo image which is scrambled through a weU known PN sequence. They showed adding a scrambled watermark to high-pass coefficients in an adaptive manner based on entropy results in a high perfonnance detection capability for watennark extraction. Xiao et al. [4] proposed an adaptive watennarking scheme based on texture and luminance features in the CT domain, which uses the texture and luminance features of the host image to find the positions in which the watennark is embedded. In [6] Mahesh et al proposed a non-blind image watennarking using contourlet transfonn. Watennark is encrypted and embedded into high frequency directional subband, which is obtained by performing contourlet decomposition on the host image. The watermarked image has very good perceptual transparency. Watennark extraction algorithm is a non-blind process, which makes use of host image as reference for retrieving the watermark. This algorithm is robust against cropping attacks and geometric attacks and also has superior Peak Signal to Noise Ratio (PSNR) for the watermarked image. Jayalakshmi et al. [7] proposed a non-blind watermarking scheme using the pixels selected from high frequency coefficients based on directional subband which doubles at every level. They observed that contourlet-based methods perfonn much better than wavelet-based methods in

ICCCNT'12 26th_28th July 2012, Coimbatore, India

IEEE-20180 images like maps. The watennark was a 16 x 16 binary logo. In [lO], a contourlet-based image watennarking method is discussed which embeds a grayscale watennark with as much as 25% of the host image size in the 16th directional subband of the host image. Since the original image is required for watennark extraction, the method is considered to be non blind. Haifeng Li,Weiwei Song,et al [9] proposed a blind watermarking algorithm in contourlet domain. The watermark which is composed of pseudo-random sequences is embedded in the selected Contourlet transform coefficients by means of multiplicative method. The Contourlet coefficients are modeled with Generalized Gaussian Distribution with zero mean, and then watermark detection method is proposed based on maximum likelihood detection. The decision rule is optimized via Neyman-Pearson criterion. Here we present a blind watermarking algorithm in contourlet transform. Iris code is taken as the watennark. The watermark is embedded into the sign of contourlet coefficients. Before embedding the watermark into the contourlet coefficients of the fingerprint, it is encrypted by a two-dimensional Arnold Transfonn. This paper is organized as follows. Contourlet Transfonn is discussed in section II of this paper. A brief discussion about iris codes is given in section III. The watermark embedding and extraction algorithm is presented in section IV. Simulation results are discussed in section V.

II. CONTOURLET TRANSFORM Images can be represented in spatial domain and transform domain. The transform domain image is represented in tenns of its frequencies; however, in spatial domain it is represented by pixels. In simple tenns transform domain means the image is segmented into multiple frequency bands. The watennarking procedure can be done in the spatial domain and frequency domain, where frequency domain is preferred due to its robustness against the different attacks perfonned on the watennarked image. To transfer an image to its frequency representation we can use several revers ibIe transfonns like Discrete Cosine Transfonn (DCn, Discrete Wavelet Transfonn (DWT), or Contourlet Domain Transform (Cn. Each of these transfonns has its own characteristics and represents the image in different ways. The contourlet transfonn is a geometrical image based transfonn that was introduced by M.N Do and M. Vitterli [1], which can efficiently represent images containing contours and textures. Even though DWT is popular, powerful, and familiar among watennarking techniques, it has its own limitations in capturing the directional infonnation such as smooth contours and the directional edges of the image. This problem is addressed by contourlet transform. The contourlet transform was developed as an improvement over wavelet where the directional infonnation is important. In addition to multiscale and time-frequency localization properties of wavelets, Contourlet Transfonn (Cn offers directionality and anisotropy. In contourlet transfonn, the laplacian pyramid (LP) [2] is first used to capture point discontinuities. It is then

followed by a directional filter bank (DFB) [3] to link point discontinuities into linear structures. The required number of directions can be specified by the user. Since contourlets gives more edges, it is more suitable for data hiding applications as more data can be hidden in the high frequency regions without perceptuaUy distorting the original image. This results in an image expansion using basic elements like contour segments, and thus called contourlet transform, which is implemented by a pyramidal directional filter bank (PDFB).

Image

LFD

Fine scale

Directional subbands

Fig 1: Contourlet Filter bank Fig 1 shows the Contourlet Filter bank. The first stage is LP decomposition and the second stage is DFB decomposition. At each level, the LP decomposition generates a downsamp\ed lowpass version of the original, and the difference between the original and the prediction results in a bandpass image. Fig 2 illustrates this process, where H and G are called analysis and synthesis ftIters, respectively, and M is the subsampling matrix. The bandpass image obtained in the LP decomposition is further processed by a DFB. A DFB is designed to capture the high-frequency content like smooth contours and directional edges.

x

(a) a

_b_'--1� (b)

Fig 2: Laplacian Pyramid (a) Analysis (b) Reconstruction The DFB is efficiently implemented via a K-Ievel binary tree decomposition that leads to 2K subbands with wedge-shaped frequency partItioning. The contourlet decomposition is illustrated by using the fmgerprint image of size 512x512 and its decomposition into 2 levels, as shown in Fig 3. At each successive level, the number of directional subbands is 8, and 16.


IEEE-20180 III. IRIS CODE

Fig 3.Two level Contourlet Decomposition offmger print image Embedding the watermark in high frequency components improves the perceptibility of the watermarked image Therefore, we have selected the highest frequency subband which possesses the maximum energy for watermark embedding. The Energy E of a subband s (i,j) is computed by

The texture in a human iris has been shown to have good individual distinctiveness and thus is suitable for use in reliable identification. A conventional iris recognition system unwraps the iris image and generates a binary feature vector. The iris is rich in textural features which can be used to describe individuals for identification [10]. In the system introduced by Daugman [11], the iris is segmented and unwrapped into a rectangular image. From the unwrapped iris, texture is extracted by applying a 2-D Gabor filter bank. This is encoded into a binary image, known as the iris code, that serves as the feature vector for recognition. In recognition, two iris codes, the embedded iris code and extracted iris code are said to match if the normalized Hamming distance between them is less than a pre defmed threshold. The normalized Hamming distance is the number of bits that differ between the codes divided by the total number of bits. The equation for the Normalized Hamming distance between two iris codes A and B is given as HD(A,B)=

AtBB

(2)

Total No.of bits

( 1) IV. THE WATERMARKING PROCESS The energy variation in the last level of the contourlet transformed fmgerprint image is shown in figure 4. It can be found that band 16 has the maximum energy. 6 x 10 2 ,---�---,----,---� 1. 8

.

.

.

.

.

.

.

.

.

.

_

.

.

. . . .

,

1.6 .... ............

.

The block diagram of watermark embedding and extraction is shown in the figure 5 and 6. The iris code is first encrypted using Arnold transform and then the embedding algorithm is applied. The number of iterations to be performed while encrypting serve as a key in the watermark extraction process. The band which we choose to embed the data also serves as a key.

1.2 >� Q) c: Q)

I

0.8

ENCRYPliON

IRIS CODE

ALGORITHM

� IV

0.6 COVER IMAGE

0.4

IFINGER PRINn

--j

CONTOURLET DECOMPOSIllON

�

SELECT SUITABLE SUB·BAND

�I

EMBEDDING ALGORITHM

0.2

sub-band

16

WAllERMARKED

no:

FINGERPRINT IMAGE

f--

INVERSE CONTOURLET TRANSFORM

Fig 4. Energy variation in the last level. Fig 5. Watermark Embedding process. The majority of coefficients in the highest frequency subband are significant values compared to the other subbands of the same level, indicating the presence of directional edges.


-

IEEE-20180

WAlERMARKED FINGER PRINT IMAGE

I-

CONTOURLET DECOMPOSITION

H

SELECT SUITABLE SUB·BANDS

IRIS CODE EXTARClED

r---;;

k:-

I'

6) Obtain the watermarked image by performing inverse contourlet transform to these modified coefficients.

EXTRACTION ALGORITHM

C.

DECRYPTION ALGORITHM

1) Perform two-level contourlet transform to the watermarked fingerprint image and consider the high frequency sub-band having the maximum energy which was used for watermark embedding.

Fig. 6.Watermark Extraction Process.

A. Watermark Image Encryption. A scrambled version of the watermark is obtained using the Arnold Transform. Encrypting watermark images can improve its security and enhance difficulty of extraction of watermark without authorization. And then sophisticating watermarks can be effectively prevented. First, the generalized Arnold mapping, map(x,y): (k,I) -+(ij) , is applied to disorder watermark images. That is mod N

(3)

Where ij,k,1 = 1,2,3 ... N

B.

Watermark extraction

2) The scrambled watermark can be extracted by detecting the sign of the contourlet coefficients. The extracted bit Ws= 1, if sign(D(i,j)==I) = 0, if sign(D(i,j)==-I) 3) Perform Arnold Anti scrambling using the same Key 'K' on Ws to get the watermark W.

V. RESULTS AND DISCUSSION In order to evaluate the performance of the proposed watermarking algorithm, we use a fmgerprint image of size (512*512) and the iris code of size (64*256). We have used fmgerprint templates from the CASIA database. The size of a iris code is 1024 bytes. Simulations were done using MA TLAB.

We choose a = 3 in our simulations. A maximum PSNR value of 43.62 dB is obtained for the watermarked fmgerprint image.

Watermark Embedding

1) The iris code taken is preprocessed to make it into a square image so that it can be encrypted using Arnold transform.

Figure 7 shows the original cover image and the watermarked image. The watermark used, which is the iris code is shown in Figure 8.

2) Perform Arnold transform to the watermark image , i.e the iris code and save the number 'K' of the scrambling as the key. 3) Perform two- level contourlet transform to the fmgerprint image and take the high frequency sub-band having the maximum energy for watermark embedding. 4) ModifY the value of the contourlet coefficient of the selected sub-band as follows: DsCi,j) = = = =

D(ij) D(i,j) -D(ij) -D(i,j)

if if if if

(sign(D(i,j)== 1) & Ws==I) (sign(D(i,j)==-I) & Ws==O) (sign (D(ij)==-I) & Ws==I) (sign (D(iJ)==I) & Ws==O)

(a) Original Carrier image (b) Watermarked image Fig 7. The original carrier image and watermarked image.

5) After changing the sign of the contourlet coefficients, each of the coefficient is modified as follows: Dw(i,j) = a* DsCij). The value of a will determine the robustness of the algorithm and the perceptibility of the watermarked fmgerprint image.

The Table 1 shows how the PSNR of the watermarked image varies as the coefficient scaling factor a varies.


IEEE-20180 hamming distance obtained in aU the cases are found to be less than the threshold. Compared to other DCT and DWT based algorithms, the PSNR obtained after embedding the watermark and after extraction of the watermark using contourlet transform is found to be superior.

TABLE 1 PSNR Variation with scaling factor (a)

(a)

PSNR of the watermarked image

1

43.62dB

2

39.11 dB

3

35.58 dB

4

33.05 dB

Coefficient scaling factor

The performance of the scheme is compared in terms of the Peak Signal to Noise Ratio (PSNR) of the watermarked image and extracted watermark. As the scaling factor a increases, the PSNR of the watermarked image decreases. This is shown in Table l.

VI. CONCLUSION This paper presents a novel algorithm for embedding and extracting data in the contourlet domain. We introduced a new and more robust method of hiding biometric data in the contourlet coefficients of the fmgerprint inage The algorithm embeds each bit of data in the sign of the contourlet coefficient. The watermark images are embedded to the cover image after encryption process in order to improve the safety of watermark images. Simulation results show that the extracted watermark closely matches with the original image and it has good robustness. The robustness of the algorithm to additive channel noise makes the method very efficient in secure transmission of biometric data. REFERENCES

PSNR Where image and

MAX MSE

,

=

20 log 1 0

is maximum

MAX!

( 4)

,-;:-;;:;;::;

"\JMSE

possible pixel value of the

[1 ]

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[2]

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[3]

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[4]

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is the Mean Squared Error. Let the host image

f(i,j) is of size NxN and the watermarked image is f'(i,j) (5) TABLE 2 Extracted Watermark under attacks a=2

a= 4

a= 3

ATTACKS PSNR

HD

(db) SALT AND PEPPER NOISE (0.0005)

16.92

GAUSSIAN NOISE (0.0005)

11.94

SPECKLE NOISE (0.0005)

16.12

PSNR

(db) 0.021

0.061

0.022

18.43

14.23

18.06

HD

PSNR

0.014

18.94

0.037

0.015

HD

(db)

16.02

19.74

0.013

0.025

0.011

The Table 2 shows the PSNR and hamming distance calculated after performing attacks on the watermarked image. The results show that the approach has good extraction effect and robustness, especially to additive channel noise. The iris codes extracted are said to be authentic if the hamming distance obtained is less than a predefmed threshold [12]. The


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