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Citation: Abdullah, Mohammed, Dlay, Satnam, Woo, Wai Lok and Chambers, Jonathon (2017) A Framework for Iris Biometrics Protection: A Marriage Between Watermarking and Visual Cryptography. IEEE Access, 4. pp. 10180-10193. ISSN 2169-3536

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URL: http://dx.doi.org/10.1109/ACCESS.2016.2623905 <http://dx.doi.org/10.1109/ACCESS.2016.2623905>

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Received September 29, 2016, accepted October 20, 2016, date of publication November 14, 2016, date of current version January 27,2017.

Digital Object Identifier 10.1109/ACCESS.2016.2623905

A Framework for Iris Biometrics Protection:A Marriage Between Watermarking andVisual CryptographyMOHAMMED A. M. ABDULLAH1,2, (Member, IEEE), SATNAM S. DLAY1, (Member, IEEE),WAI L. WOO1, (Senior Member, IEEE), AND JONATHON A. CHAMBERS1, (Fellow, IEEE)1School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K.2Department of Computer and Information Engineering, Ninevah University, Nineveh 41002 Iraq

Corresponding author: M. A. M.ABDULLAH ([email protected])

This work was supported by the Ministry of Higher Education and Scientific Research, Iraq.

ABSTRACT This paper presents a novel security architecture for protecting the integrity of iris imagesand templates using watermarking and visual cryptography (VC). The proposed scheme offers a completeprotection framework for the iris biometrics which consists of two stages: the first stage is for iris imageprotection, while the second is for the iris template. First, for protecting the iris image, a watermark textwhich carries personal information is embedded in the middle band frequency region of the iris image usinga novel watermarking algorithm that randomly interchanges multiple middle band pairs of the discrete cosinetransform. Second, for iris template protection, the binary iris template is divided into two shares using VC,where one share is stored in the database and the other is kept with the user on a smart card. In addition,the SHA-2 hash function is utilized to maintain the integrity of the stored iris template in both the databaseand smart card. The experimental and comparison results on the CASIA V4 and UBIRIS V1 iris databasesdemonstrate that the proposed framework preserves the privacy of the iris images and templates and retainsrobustness to malicious attacks, while it does not have a discernible effect on the recognition performance.

INDEX TERMS Biometrics, iris recognition, security and privacy protection, watermarking, smart card,template security, visual cryptography.

I. INTRODUCTIONDespite the fact that biometric systems offer reliabletechniques for personal identification, their usage could behampered by the lack of a proper protection scheme thatguarantees the security and privacy of the biometric traits.When biometric images or templates are transmitted throughinsecure channels or stored as raw data, they run risks of beingstolen or modified. Hence, it is imperative that robust andreliable means of biometric protection are implemented [1].

Ratha et al. [2] described eight types of attacks that arepossible in a biometric system, such as database templatetampering, template modification, the matcher override of thefinal decision and attack on the channel between the featureextractor and the matcher, or attack on the channel betweenthe database andmatcher.Moreover, due to thewide spread ofbiometrics technology in many applications, it is very likelythat biometric data are being transmitted over non securechannels. Hence, for a biometric system to work properly, the

system must guarantee that the biometric data came from alegitimate person at the time of enrolment.

Several means are employed to protect biometric data suchas only encryption, or watermarking and encryption. Encryp-tion can be used as one potential mechanism for protectingthe biometric features as in [3]. However, encryption maylimit the capacity of large scale biometric systems becauseit can be computationally expensive. In addition, encryptioncannot provide complete protection as the templates must bedecrypted before matching. Jain et al. emphasized this in [4]by suggesting that if only cryptographic techniques are usedfor the protection of biometric data, security of such data isnot fully maintained because this data has to be decryptedsomewhere.

Therefore, the use of watermarking technology hasemerged. Since watermarking involves hiding informationwithin the host data, it can provide security even after decryp-tion. On the other hand, Visual Cryptography (VC) can be

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M. A. M. Abdullah et al.: Framework for Iris Biometrics Protection: A Marriage Between Watermarking and VC

utilized for biometric template protection. The most com-monly used template for an iris recognition system is theso called IrisCode which is a binary compact representa-tion of the iris [5]. This template is usually stored as rawdata in databases or transmitted over unsecured channels.It was believed in the biometrics community that this typeof iris representation does not reveal adequate information toregenerate the iris image as the encoding process is a one-way function. However, recently researchers [6], [7] wereable to propose a reversibility scheme for the IrisCode. Forinstance, the authors in [7] proposed a reconstruction methodfor iris images from a binary template using a probabilisticapproach based on genetic algorithms where they analyzedthe vulnerabilities of commercial iris recognition systemsby matching the reconstructed synthetic images against theoriginal ones. Their experiments showed the fragility of suchsystems against this type of attack.

Due to the aforementioned points, it is imperative to findrobust template protection methods. In this paper, we there-fore propose the first work which considers enhancing thesecurity of the iris biometrics through both watermarking andVC. The proposed framework for iris biometrics protectionincorporates two stages. The first stage is a robust water-marking algorithm to protect the evidentiary integrity of theiris images based on randomly exchanging four middle bandcoefficient pairs of the DCT to embed text data as a contex-tual watermark in the iris image. The second stage is a VCscheme for iris template protection that neither involves pixelexpansion nor quality loss in the iris template. Therefore, afterdecomposing the iris template into two shares, one share isgiven to the user on a smart card and the other share is storedin the database along with a signature generated by a hashfunction. Furthermore, the integrity of the stored iris templateis also guaranteed by using the hash signatures.

This paper is organized as follows. The next sectionpresents a literature review on the related works whileSection III gives an overview of the watermarking algorithmsand visual cryptography. The proposed method is explainedin Section IV. Experimental design and performance analysisare given in Section V. Finally, Section VI concludes thispaper.

II. RELATED WORKSVarious algorithms have been suggested to protect biometricdata using watermarking. Park et al. [8] proposed a water-marking method to embed the iris feature inside a faceimage and tested their method under different attacks.Hassanien et al. [9] suggested a watermarking techniquebased on the Discrete Wavelet Transform (DWT) to embediris data into the content of a digital image in order to identifythe owner. However, no experiments have been undertakenon the effect of the watermarking on the iris recognitionperformance. Later, the authors in [10] applied a biometricwatermarking by taking the DWT and the Singular ValueDecomposition (SVD) of the host image to obtain eigenvectors. Next, the iris features were extracted with the DCT

to obtain 200 coefficients and then embedded in the eigenvector derived from the host image. Despite the good resultsreported by the authors, the drawback of this approach isthat the feature extraction algorithm for the iris cannot bechanged. The work in [11] applied watermarking to hide thefingerprint and iris features in a cover image. The cover imageis divided into blocks then each block is transformed witha two-dimensional DCT and classified as a smooth blockor edge block. The biometric features are embedded in thelow frequency coefficients of the 8 × 8 DCT blocks whilethe edge blocks are eliminated. However, removing the edgeblocks could unfortunately result in quality degradation ofthe original image. A watermarking algorithm is proposedin our previous work [12] to protect the iris images basedon interchanging fixed locations of the DCT middle bandcoefficients. However, there is a possibility that an attackercan predict these fixed locations and destroy the watermarkinformation or embed false watermark data. In addition, theiris template is still not protected as the aim of that work wasto protect the iris images only.

It is evident from the previous works that watermarkingcan be used effectively to protect the integrity of biometricimages. However, watermarking cannot be used for biometrictemplate protection because watermarking introduces somedegradation to the host medium. In a biometric system,any degradation to the template is not acceptable becausesuch degradation is going to affect the system performancesignificantly. Therefore, alternative approaches are needed.Hao et al. [13] presented a scheme for integrating the irisbiometric into cryptographic applications. The iris codeis encoded with binary keys using XOR operation whileHadamard and Reed-Solomon codes are used to resolve thevariability in the iris code. Good results are reported in termof False Reject Rate (FRR) however, the iris images wereideal and according to [14] a high FRR was recorded whenthe approach is applied on the ICE database. In addition,the 44 security bits used in this method is inadequate in thecurrent cryptographic applications [15]. Cimato et al. [16]proposed a multi-biometric system which extracts an identi-fier (ID) from the templates of left and right irises of eachuser with the help of a hash function and pseudo randompermutation function. In the verification phase, both the tem-plates and user’s ID are required to complete the authen-tication. Although the proposed method can offer a secureID generation, we remark that the iris template is still notprotected. In addition, this method requires two biometrictraits to generate the ID which adds more complexity.

More recently, Yan et al. [15] proposed a method topreserve the privacy of the biometric credential. Their methodfuses the user’s biometrics (iris image) with a referencesubject using keys extracted from the user’s biometrics togenerate a BioCapsul which could be used later instead ofthe biometric template for the authentication. However, thismethod degrades the iris recognition performance. In addi-tion, generating the BioCapsul requires more complex oper-ation compared to the simple VC scheme that we propose

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in this paper. Rathgeb et al. [17] proposed a scheme for iristemplate protection based on Bloom filters. Although theusage of Bloom filters enables irreversibility for a uniformiris template, the scheme does not provide unlinkability. Thisconcern is reinforced by the work of Hermans et al. [18]which demonstrated that the scheme is vulnerable tocross-matching attacks.

VC is a robust way of protecting an image withoutcomplicated mathematical operations or any knowledge ofcryptography. Although there are some methods for protect-ing the biometric traits with VC [19], [20], few enhance thesecurity of iris biometric through VC. The authors in [19]proposed a method to secure the face image with the helpof stenography and VC to decompose the image into twoshares where each share is stored in a different database sothat the original image can be revealed only when both sharesare available. The drawback with the previous method is theadditional cost of having two database servers for saving theshares and the possibility of tampering the templates in thesedatabases. In [20] the authors proposed a method to improvethe privacy of the face images using half-toning and VC tosplit the face images into two encrypted parts. However, theefficiency and robustness of this method were not supportedby experimental results or security analysis. In addition, noinformation is given on how to deal with the loss in contrastor the problem of resolution expansion of the resultant imagein the aforementioned methods.

In this paper, we fill the gap in iris biometrics security byproposing a two-stage framework for iris images and templateprotection. The first stage is a robust watermarking algorithmto protect the evidentiary integrity of the iris images based onexchanging multiple middle band coefficients of DCT blocksusing text data as a contextual watermark. The second stage isprotecting the iris template with VC by dividing the templateinto two secret shares.

III. WATERMARKING ALGORITHMS ANDVISUAL CRYPTOGRAPHYAs the proposed approach is based on two main schemes,namely watermarking and visual cryptography, this sectionprepares the reader by giving a brief overview of watermark-ing and visual cryptography algorithms and highlights whythe DCT has been selected as the transform basis of theproposed watermarking algorithm.

A. WATERMARKING ALGORITHMSA number of watermarking techniques are available forembedding information securely in an image. Watermarkingalgorithms can be classified according to their embeddingdomain into transformation domain techniques [21] and spa-tial domain techniques [22]. In the spatial domain the pixelvalues are directly modified to embed the watermark usingdifferent approaches such as Least Significant Bit (LSB) [23]or the correlation-based technique [24]. While the spatialdomain techniques have least complexity and high payload,they cannot withstand low pass filtering, image compression

and common image processing attacks [25]. Therefore, trans-form domain watermarking has emerged because it is robustagainst image manipulations and compression. In the fre-quency domain, the host image is segmented into multiplefrequency chains using several transformations such as DWTor DCT [21], [26]. Then, the inverse transform is applied toobtain the watermarked image.

In principle, any frequency domain transform can be usedfor image watermarking, however, frequency transformationin the DCT domain allows an image to be divided intodifferent frequency bands, so they facilitate embedding thewatermarking information in a specific frequency band [27].It has been found that the middle frequency bands are mostsuitable for embedding the watermark because the low fre-quency band carries the most visual important parts of theimage while the high frequency band is exposed to removalthrough compression and noise attacks on the image. There-fore, embedding the watermark in the middle frequencyband neither affects the visual important parts of the image(low frequency) nor overexposes them to removal throughattacks when high frequency components are targeted [27].

B. VISUAL CRYPTOGRAPHYVC is a secret sharing scheme which was introduced byNaor and Shamir [28] to decompose an image into n randomshares such that, when all these shares are superimposed, theoriginal image is revealed again. However, the secret imagewill not be revealed if the number of stacked shares is lessthan n.

FIGURE 1. (2,2) visual cryptography; the 50% loss in contrast can besolved when using the XOR operation.

In the case of (2,2) VC, each pixel in the binary image Iof size (M × N ) is encoded into two subpixels which aredenoted as SR1 and SR2 as illustrated in Fig. 1. If the pixelis white, one of the two rows in Fig. 1 corresponding to thewhite pixel is randomly chosen to generate SR1 and SR2 andvice versa for a black pixel. Therefore, neither SR1 nor SR2divulges any information about the binary image I . The orig-inal image can be reconstructed again by superimposing SR1and SR2 together. However, the resulting image will be of size(M × 2N ).Although traditional VC is a simple and a powerful protec-

tion scheme, unfortunately, it has not been widely used due tothe increase in image size and the 50% loss in contrast [29].The problem of contrast loss can be solved by superimposing

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the shares together using the XOR operation instead of theOR (as shown in Fig. 1) and hence no loss in quality will occurin the original image. Nevertheless, the generated image isstill twice the size of the original one. In the next section wepropose an approach to remedy this problem.

IV. THE PROPOSED METHODIn this section, we propose a two-layer iris images andtemplates protection scheme. The first stage is a robustwatermarking algorithm to protect the iris images based onrandomly exchanging the middle band coefficients of theDCT blocks using text data as a contextual watermark whilethe second stage is focusing on protecting the iris templatewith VC.

A. STAGE ONE: IRIS IMAGES WATERMARKINGThe proposed watermarking algorithm is designed in a waywhich will not degrade the iris image or the recognitionperformance while it retains robustness to malicious attacksand noise. Hence, the proposed algorithm encodes one-bitof the binary watermark text into each 8 × 8 sub-block ofthe host image by ensuring that the difference of two mid-band coefficients is positive in the case the encoded valueis 1. Otherwise, the twomid-band coefficients are exchanged.Accordingly, after the DCT is applied to the image,an 8 × 8 block is taken. Each DCT block consists of threefrequency bands as illustrated in Fig. 2. FL and FH standfor the low and high frequency components of the blockrespectively, while FM is the middle frequency band and ischosen for embedding watermark information. This avoidssignificant modifications to the cover image while providingadditional resistance to lossy compression techniques whichtarget the high frequency components [30].

FIGURE 2. Frequency regions in an 8 × 8 DCT block [27].

The proposed scheme targets the middle frequencyband FM so that two locations from the DCT block (DCTu1,v1and DCTu2,v2) are chosen as the region for comparison.Firstly, the watermark text is converted to a binary imageand each pixel inside the watermark text is checked so thecoefficients are swapped if the size of each coefficient doesnot agree with the bit that is to be encoded. Thus, if thepixel value in the binary text is 1, the DCT coefficient areswapped such that DCTu1,v1 > DCTu2,v2. On the other hand,

if the pixel value is 0, coefficients are swapped so thatDCTu2,v2 > DCTu1,v1.

Hence, instead of degrading the image by inserting data,this scheme hides the watermark by interpreting 0 or 1 withthe relative values of the two fixed locations in the FMregion (DCTu1,v1 and DCTu2,v2). It is known that the DCTcoefficients of the middle frequencies generally have similarmagnitudes [31] so swapping of such coefficients will notalter the watermarked image significantly.

During the watermark extraction, the 8 × 8 DCT of thecover image is taken again, and the watermarking algorithmwill decode a 1 if DCTu1,v1 > DCTu2,v2; otherwise it willdecode a 0 to form the watermark.

Yet, if only one pair of coefficients is used to hidethe watermark data, it will become vulnerable to noiseand attacks. Therefore, the watermark can be destroyed byimage manipulations and compression (as we demonstratein Section V-A.5). In addition, an attacker can analyze somewatermarked copies of the same image to predict the loca-tions of these coefficients as well as destroy them. To solvethese problems, we propose to exchange multiple coefficientpairs by selecting random locations from the FM frequencyband for the embedding purpose. Exchanging more than onepair will increase redundancy and make the scheme robustagainst different attacks. In addition, the random selectionof the embedding locations in each DCT block makes italmost impossible for the attacker to predict these locationsand destroy the watermarking information or embed falseinformation inside the watermarked image.

FIGURE 3. JPEG quantization table and the selected embedding locations.

Empirical results have shown that exchanging four pairsfrom the middle frequency band gives a good trade-offbetween robustness and perceptibility. Basically, any of the22 middle band locations (FM ) shown in Fig 2 can be uti-lized for the embedding purpose. Nevertheless, to make the

TABLE 1. The locations array (L) used for selecting the watermarkingembedding locations.

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Algorithm 1: Embedding AlgorithmInput: s1,L,W ,X

(s1: watermarking key, L: locations array, W :watermarking text, X : host image)

Output: Y(Y : watermarked image)

1: for i = 1→ size(W ) do2: X8×8(i) = X ;

{subdivide the host image (X ) into blocks of 8× 8pixel}

3: XDCT (i) = 2D-DCT (X8×8(i)){Compute the 2D-DCT of each 8× 8 block of thehost image}

4: for each DCT block, generate 4 random numbers rwithin the range of 1−8 based on the private key s1.

5: if W(i) = 0 then6: for j = 1→ 4 do7: p = r(j) {select one of the random locations}8: exchange DCT coefficients to meet this

condition DCT (Lp,1) < DCT (Lp,2){Now adjust the four values such that theirdifference becomes larger than the strenghthconstant s, thus:}

9: if DCT (Lp,1)− DCT (Lp,2) < s then10: DCT (Lp,1) = DCT (Lp,1)+ s/211: DCT (Lp,2) = DCT (Lp,2)− s/212: end if13: end for14: else if W(i) = 1 then15: for j = 1→ 4 do16: p = r(j) {select one of the random locations}17: exchange DCT coefficients to meet this

condition DCT (Lp,1) >= DCT (Lp,2){Now adjust the three values such that theirdifference becomes larger than the strengthconstant s, thus:}

18: if DCT (Lp,2)− DCT (Lp,1) < s then19: DCT (Lp,2) = DCT (Lp,2)+ s/220: DCT (Lp,1) = DCT (Lp,1)− s/221: end if22: end for23: end if24: Take inverse DCT to reconstruct Y25: end for

algorithm robust against JPEG compression, eight embed-ding pairs have been selected based on the recommendedJPEG quantization table. It can be seen from Fig. 3 thatthese locations are suitable for embedding because theyhave almost the same value in the JPEG quantization table.Therefore, a scaling applied to any of these coefficientswill scale the other one with the same factor. This in turnwill preserve the relative size of the coefficients to beexchanged.

Algorithm 2: Detection AlgorithmInput: s1,L,Y

(s1: watermarking key, L: locations array, Y :watermarked image)

Output: W(W : binary text)

1: for i = 1→ size(W ) do2: for each DCT block, generate the same 4 random

numbers r within the range of 1− 8 based on theprivate key s1.

3: Y8×8(i) = Y ;{subdivide the cover image (Y ) into blocks of 8×8pixels}

4: YDCT (i) = 2D-DCT (Y8×8(i)){Compute the 2D-DCT of each 8× 8 block of thecover image}

5: for j = 1→ 4 do6: p = r(j) {select one of the random locations}7: if DCTp,1 > DCTp,2 then8: W (i) = 19: else10: W (i) = 011: end if12: end for13: reconstruct the binary text image W from W(i)14: end for

Thence, the location of the pairs to be exchanged will beselected randomly from the shaded locations shown in Fig. 3based on a private key s1 which is stored on a smart cardas illustrated later in Section IV-B1. This key (s1) is used asan initial seed for the random number generators which willgenerate the four digits vector r within the range of 1 to 8.Hence, four new rows from the location array L shown inTable 1 will be chosen for each 8×8 block of the cover imagebased on the random numbers. For example, if r = [2 3 5 8]this means that the 2nd , 3rd , 5th and 8th rows from the locationarray L will be chosen and the corresponding pairs will beused for the embedding purpose. So, when the 8th row fromthe location array (L) is selected, this means that p = 8 andDCT (Lp,1) is (2,6) and DCT (Lp,2) is (3,6) which correspondto the values of 58 and 57 in Fig. 3, respectively.

Moreover, to improve the robustness of the watermarkingalgorithm, we propose to add a watermark strength constants such that DCTu1,v1 − DCTu2,v2 > s. If coefficients do notmeet this criterion, a constant value will be added to satisfythe relation.

1) EMBEDDING ALGORITHMEach 8 × 8 block of image will be used to hide one bitof watermark text. A binary text image (W ) is taken as awatermarking object which can be interpreted as a 1-D arrayof 1s and 0s. The watermark text image carries the person’sbio-information such as name, ID and date of birth. The steps

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FIGURE 4. Block diagram of the proposed watermarking algorithm.

of the embedding algorithm are shown in Algorithm 1 whilethe flow chart is depicted in Fig. 4.

2) THE STRENGTH OF WATERMARKThe robustness of the watermark has been increased bychoosing an appropriate value of the strength constant s.Increasing s will degrade the image but it will reduce thechance of errors during the detection phase. Experimental

results indicate that setting s equal to 15 is the most suitablevalue in terms of perceptibility versus robustness. Therefore,the experiments have been conducted by keeping s = 15 forall the images.

3) DETECTION ALGORITHMWatermark extraction is the reverse procedure of the water-mark embedding algorithm. The embedding location will be

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FIGURE 5. The enrolment module of the proposed method using VC.

FIGURE 6. The authentication module of the proposed method using VC.

selected based on the private key (s1) which is either obtainedfrom the smart card or generated from the stored templatein the database (share1) with the help of a hash functionas demonstrated later in Section IV-B.1. The steps of thedetection algorithm are shown in Algorithm 2.

B. STAGE TWO: VISUAL CRYPTOGRAPHYAttacks on the stored biometric template in a database areconsidered as one of the most destructive attacks becausethey lead to different vulnerabilities [32] such as gaining

an unauthorized access to the system by creating a physicalspoof from the template [6], [7], [33]. In addition, they allowan unauthorized access to the system by the replication of thestolen template to the matcher. Moreover, the stored templatecould be replaced by an imposter. In this section, we propose anovel template protection scheme in order to tackle the aboveproblems. Furthermore, to protect the integrity of the templatesaved on the card or database, the SHA-2 hash function isused. SHA-2 is a set of one way hash functions designed byNSA [34] which generates a unique signature of a vector.Therefore, the hash function is implemented to generate a

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FIGURE 7. Down sampling to retrieve the original template size.

unique signature of the template as will be discussed in thenext sub-section. The proposed template protection methodconsists of two modules namely: (A) Enrolment module and(B) Authentication module.

1) ENROLMENT MODULEIn this module, feature encoding is implemented by convolv-ing the normalized iris template with a 1-D log-Gabor filter.The output of filtering is then phase quantized to four levelsusing the Daugman method [5], with each filter producingtwo bits of data for each phase to form the binary template(IrisCode).Then, the binary template is decomposed into two shares

using (2,2) VC and the original template is discarded.After that, two 256 bit signatures s1 and s2 are generatedas signatures for share1 and share2 respectively using theSHA-256 hash function to maintain the integrity of the iristemplate. In the enrolment stage, one of the decomposedshares (share1) is stored in the database along with s2 (signa-ture of share2) while share2 and s1 are given to the user ona smart card. s1 is also used as the private key which selectsthe watermarking embedding locations. The enrolment stageis shown in Fig. 5.

2) AUTHENTICATION MODULEDuring the authentication process, the system sends a requestto the database to fetch the corresponding (share1) based onthe generated signature (s2) from share2. Then, the obtainedshare from the database is stacked together with the user’sshare from the smart card in order to reconstruct the originaliris template. Moreover, to make sure that the template inthe smart card or the database is not altered, the SHA-256hash function is generated again and compared with thestored signatures s1 and s2. If the signatures do not match,authorization will not be granted. After that, the inbounduser’s iris template and the reconstructed iris template fromthe smart card and the database are compared together toauthenticate the user. The authentication module is illustratedin Fig. 6.

In order to restore the original template size after the expan-sion by VC, the reconstructed template is down sampled byselecting only one pixel from every 2 × 2 block as shownin Fig. 7. Thus, the generated image will have the same sizeof the original template and less storage and computationalrequirements.

V. EXPERIMENTAL DESIGN AND RESULTSThe proposed method has been tested on the CASIA V4and the UBIRIS V1 databases. The CASIA V4 was releasedby the Institute of Automation, Chinese Academy of Sci-

FIGURE 8. Perceptibility of the watermarked image; (a) original image,(b) watermarked image, (c) the difference image, (d) original watermark,(e) binarized text and (f) the extracted watermark.

ences [35]. It contains 16212 images from 819 classes. It wascollected in an indoor environment under the near-infraredlight. On the other hand, the UBIRIS V1 iris image databasewas released by the University of Beira, Portugal [36]. It con-tains 1877 images from 241 subjects in two different sessions.All images are taken under visible light.

For the first stage, the iris images were watermarked withthe 64 × 64 pixel text image shown in Fig. 8 (d) after con-verting it to a binary image. On the other hand, after featureextraction, the generated template is decomposed with (2,2)VC into two shares: one share is saved in the database andthe other is saved on a smart card with template on cardarchitecture as proposed in the previous work [37]. In the nextsub-sections, an analysis is presented for each stage of theproposed scheme.

A. STAGE ONE: WATERMARKINGA good watermarking algorithm should meet differentrequirements such as perceptibility, robustness to variousimage manipulations and it should not degrade the matchingperformance of the biometric system significantly. In order toevaluate the proposed watermarkingmethod, a set of differenttests has been carried out as shown in the next sub-sections.

1) WATERMARK PERCEPTIBILITYThe similarity between the original and the watermarkedimage (perceptibility) indicates the impact of the watermark-ing algorithm on the cover image. Therefore, in a goodwatermarking algorithm, the watermarking effect should beimperceptible to the user. Fig. 8 (c) demonstrates that thedifference between the original and the watermarked irisimage is not noticeable to the naked eye without the help ofthe image processing techniques.

To quantitatively evaluate the performance of the water-marking algorithm, Peak Signal to Noise Ratio (PSNR) andBit Error Rate (BER) are calculated. The average PSNRbetween the original iris and the watermarked iris is 38.47 andthe average BER is 0.22% while the average PSNR and BERof the extracted watermarking text are 84.63 and 0.023%,respectively.

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FIGURE 9. Effect of the proposed watermarking algorithm on the iris recognition performance: (a) UBIRIS V1 and (b) CASIA V4.

TABLE 2. BER and PSNR of the extracted watermark after different manipulations using different watermarking algorithms.

FIGURE 10. Effect of different manipulations on the iris recognition performance: (a) UBIRIS V1 and (b) CASIA V4.

2) EFFECT ON MATCHING PERFORMANCETo investigate the effect of the proposed watermarkingalgorithm on the iris recognition performance, Daugman’sapproach [5] for iris recognition has been implementedwith the segmentation algorithm proposed in our previouswork [38], then the Equal Error Rate (EER) is calculated forthe non-watermarked iris images. After that, the proposedwatermarking algorithm is applied on the same iris imagesand the EER is calculated again. Fig. 9 illustrates the effectof the proposed watermarking algorithm on iris recognitionperformance in terms of the Receiver Operating Characteris-tics (ROC) curve and EER. According to Fig. 9, the proposedwatermarking algorithm barely disturb the EER across bothUBIRISV1 andCASIAV4 databases. Consequently, the pro-

posed watermarking algorithm does not involve a noticeableeffect on the iris recognition performance.

3) PERFORMANCE AGAINST COMPRESSION AND NOISEOther factors that may degrade the images were tested heresuch as compression and noising. In fact, the compressionof large images becomes inevitable when transmitting suchimages over low bandwidth channels. On the other hand,there is an increased susceptibility to image degradation dueto the noise in such channels.

To simulate these factors, the image compression algo-rithm, Joint Photograph Expert Group (JPEG) has beenapplied with different quality factors (Q) on the watermarkediris images. In addition, we applied additive zero mean white

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FIGURE 11. Effects of various attacks on the extracted watermark usingdifferent watermarking algorithms.

Gaussian noise (AWGN) to the watermarked iris imageswith zero mean and variance equal to 10−3. Table 2 clearlyindicates that the extracted text is still discernible even afteradding a Gaussian noise or applying JPEG compression withdifferent quality factors.

4) PERFORMANCE AGAINST IMAGEMANIPULATIONS AND ATTACKSThe proposed watermarking algorithmwas tested against var-ious number of imagemanipulations such as median filtering,histogram equalization, compression, cropping and noising.In addition, the BER and PSNR of the extracted text werecalculated for each type of manipulation as shown in Table 2.

In addition, the recognition performances of the water-marked iris images are compared with the manipulated irisimages in terms of ROC curves and EER as shown in Fig. 10.The slight degradation in the recognition performance inthese cases is due to the added noise factors and not due tothe watermarking algorithm.

5) COMPARISON WITH OTHER WATERMARKING METHODSIn order to appreciate the efficiency of the proposed method,different image watermarking methods such as LSB [39],Code Division Multiple Access (CDMA) [40] and DWTwith Pseudo Noise (DWT-PN) [41], [42] are implementedand compared with our method and the same manipulationsare applied to the watermarked iris images. Moreover, theproposed watermarking scheme is implemented with differ-ent strength constants as shown in Fig. 11 which depictsthe extracted watermark after various manipulations usingdifferent watermarking methods.

As expected the LSB technique could not tolerate most ofthe attacks and the watermarked image is destroyed; whereas,CDMAandDWT-PN perform slightly better than LSB by tol-erating JPEG compression, however, they failed to overtakeseveral types of manipulations. On the other hand, exchang-

ing only one pair of DCT coefficients failed to withstandall attacks because the coefficient can be easily destroyedby noise. It is noticed that there is a prominent improve-ment in the performance of the watermarking algorithm afteradding the proposed strength constant but it is still vulnerableto median filtering and compression. On the contrary, theproposed algorithm which randomly exchanges four pairswith a strength constant (s = 15) sustained all the aboveimagemanipulations and demonstrated that thewatermarkingscheme is resistant to different types of attacks.

B. STAGE TWO: VISUAL CRYPTOGRAPHYThe proposed scheme achieves the template protectionrequirements namely: revocability, diversity, security andperformance maintenance [32], [43]. Firstly, in terms ofrevocability, whenever the iris template stored in the smartcard/database is compromised, a new iris template can begenerated and decomposed into new shares. Nevertheless,to boost the security, this operation is recommended to becarried out at regular intervals. Secondly, as for diversity,the shares appear as random noise, therefore, it is hardto match them across the database as demonstrated laterin Section V-B.3. Thirdly, the iris template is secured afterthe VC because the iris template can only be generated ifboth shares are available simultaneously. Therefore, even ifthe smart card is lost or stolen, the attacker will not be ableto generate the iris template. Fourthly, the performance of thebiometric system is not affected by the proposed scheme asusing the original template or the generated template fromVC gave the same EER in both cases.

In the next sub-sections, we carry out different statisticaltests in order to check for randomness in the encrypted tem-plate and check its ability to confront the statistical attacks.

1) ADJACENT PIXELS CORRELATIONTo test the randomness of the generated shares after VC, thecorrelation coefficient [44] of the adjacent pixels is tested.In a random image, the adjacent pixels should have littlecorrelation amongst them.

TABLE 3. Adjacent pixel correlation coefficient.

Table 3 lists the averages of the horizontal, vertical anddiagonal correlation coefficients of the adjacent pixels for thetemplate, share1 and share2 respectively. It can be seen fromTable 3 that there is a very little correlation among the pixelsof the shares while the original template pixels have a highcorrelation.

2) PIXEL DISTRIBUTION TESTThis test is used to check the distribution of the pixels in theresulting share. A random vector should have almost a uni-

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form distribution of zeros and ones. To check the distribution,the number of pixels in each row of the original templatesis counted and plotted, then it compared with the encryptedshares before down-sampling. Fig. 12 shows that the pixelsof the encrypted share are uniformly distributed across all thecolumns which confirms the random nature of the resultantshares.

FIGURE 12. Pixels distribution: (a) the original iris template and (b) theencrypted share.

3) SHARE TO TEMPLATE MATCHINGIn this test, the possibility of share matching against a tem-plate is investigated. The procedure consists of matching aprobe against thewhole gallery in the database. The shares areused after down sampling as probes and the original templatesare used as a gallery. The result in terms of EER is equal to47%which clearly indicates that it is not possible to authorizea person with either of the shares. On the other hand, using theoriginal template or the generated template from VC gave thesame EER in both cases. These results clearly indicate thatthe iris recognition performance is not affected when usingthe proposed VC scheme.

4) UNLINKABILITY OF PROTECTED SHARESUnlinkability means that protected templates should notallow cross-matching so that protected templates gener-ated from a single object should differ from each other(diversity) [18]. The unlinkability of the shares is tested bymatching a probe against the whole gallery in the database.The shares are used as a gallery and each share is used asa probe to perform cross-matching. This resulted in EERequals to 48.5% which clearly indicates the unlinkability ofthe shares.

C. COMPUTATION TIMEAll experiments were conducted on a 3.2 GHz core i5 PCwith8 GB of RAM under the Matlab environment. The proposedmethod can be divided into two stages namely: watermarkingand VC.

The proposedwatermarking algorithm is based on theDCTwhich is widely used in real time devices. This is becausethe DCT can be implemented easily as it is based on realcosine basis functions that are easy to compute and imple-ment [49]. Although the direct application of these formulaswould require O(N 2) operations, the same formula can beimplemented with only O(NlogN ) complexity by factorizingthe computation with a similar approach to the fast Fouriertransform [50]. On the other hand, the VC scheme proposedin this paper utilizes the logical XOR operator which ismapped to a single-cycle operation on modern processors.Therefore, the overall complexity of the proposed approachcan be approximated to [O(NlogN ) + O(N )] which can beapproximated to O(NlogN ) for large values of N .

TABLE 4. The average computation times (in sec) of each stage in theproposed algorithm.

Table 4 shows the average computation time for each stageof the proposed method in each database. The variations inthe computation times among databases in the watermarkingstage are due to the different images size. The proposedmethod takes less than a second to be executed which impliesthat it is suitable for real time and practical in many appli-cations. Although the reported computational time of theproposed scheme is short, this time can be drastically reducedwith code optimization and porting to a compiled languagesince there are no involved computations.

D. COMPARISONS WITH STATE-OF-THE-ART METHODSIn order to achieve a fair comparison, each protection layerin the proposed protection scheme is considered individually.In terms of watermarking, comparison with the same sce-nario is difficult because most of the papers in the literaturefocus on protecting the biometric data with watermarkingby embedding such data in a cover signal [10], [11], [45].However, a few focus on protecting the biometric imageitself [12]. Table 5 lists the few available state-of-the-art bio-metric watermarking methods. The work of [10] adopted theDWT and SVD to embed the iris feature inside an arbitrarycover image. However, the watermarked image and a keyare required during the watermarking extraction stage [10].Similarly, the work of [45] exploited the DWT and SVD toembed the fingerprint feature inside a face image. However,this method requires a shuffling key to encrypt the water-marked image and the performance of this method is affectedby noise [45]. In addition, the aforementioned methods used

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TABLE 5. Comparisons with state-of-the-art biometric watermarking methods.

TABLE 6. Comparisons with state-of-the-art iris template protection methods.

the SVD which has a complexity of O(N 2) in its best imple-mentation [51]. In the work of [11], a watermarking algorithmis proposed to embed the fingerprint and iris feature in the lowfrequency coefficients of the DCT blocks of a cover image.The cover image is then divided into smooth and edge blockswhere the latter are eliminated. Unfortunately, embedding thewatermark in the low frequency AC coefficients is vulnerableto attacks [27]. In addition, removing the edge blocks willdegrade the watermarked image. In our previous work, weproposed to protect the iris images based on interchangingfixed locations of the DCT middle band coefficients [12].However, there is a possibility that an attacker can predictthese fixed locations and destroy the watermark informationor embed false watermark data.

In terms of template protection, as mentioned before,few papers address biometric template protection with VC.Since the same scenario is not available, we compare ourproposed VC method with the similar template protectionscenarios proposed in [15], [17], and [46] as shown in Table 6.A method is proposed in [15] for preserving the privacy ofthe iris template. This method involves some performancedegradation as well as complex operations to generate theBioCapsul and requires keys to be extracted from the user’ssignal. Next, an alternative method is proposed in [17] toprotect the binary iris template based on bloom filters. How-ever, Hermans et al. [18] demonstrate that the work of [17]is vulnerable to cross-shares matching and the reversibility ispossible for a nonuniform randomly generated template. Onthe other hand, the cancelable biometric approach is proposedfor iris template protection in [46]. Yet, the work of [47]demonstrated that there are several drawbacks of using thecancelable biometrics such as reversibility and venerabilityto spoofing and coalition attacks.

In this paper, all the previous drawbacks have beenaddressed since the proposed watermarking approach isrobust against noise and considered as a ‘‘blind watermark-

ing technique’’ as it does not require the original image forextracting the embedded watermark. In addition, the pro-posed watermarking method makes no restrictive hypothesison the biometric image and hence it is applicable to multi-ple biometrics traits. On the other hand, our proposed VCscheme does not involve any effect on the iris recognitionperformance and does not require any key for the decryption.Moreover, integrating the hash function with the smart cardwill maintain the integrity of the stored data and offers robust-ness against data modification attempts in either the smartcard or the database.

E. APPLICABILITY AND LIMITATIONSThe proposed scheme is designed to effectively protect theimage and template of the iris biometric. One of the mainadvantages of our proposed watermarking scheme is that itcan be readily applied to any type of images other than theiris images. Yet, the type of the embedded data should bebinary data such as a binary image or an ASCII code. Sinceone bit can be hidden in each image block, the maximum sizeof the embedded data is equal to (height × width)/blocksize.For example, for a block size of 8, if an image resolutionis 800 × 600, the maximum watermark size will be7500 bits.

On the other hand, the proposed VC scheme iscapable of working with binary template or data. This typeof biometric template is widely used in feature represen-tation of various biometric traits such as iris, face andfingerprint [5], [52], [53].

The stored template in the database has almost the samesize of the conventional IrisCode with only 256 additionalbits for the hash function. In addition, the watermarked irisimage has a similar size to the original iris image. This makesit analogous to available iris recognition systems in terms ofdatabase handling.

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VI. CONCLUSIONSThe work proposed in this paper aimed to bring insight intothe problem of biometric security. Novel schemes were pro-posed for iris image and template protection which consist oftwo security layers. The first layer is a robust watermarkingalgorithm which was implemented to protect the integrity ofthe biometric image. In particular, a binary text image thataccommodates the bio data of the person to be authenticatedwas embedded in the iris image by randomly interchangingfour pairs of the DCT middle band coefficients. The embed-ding locations were randomly selected based on a private key.Moreover, the proposed strength constant s was included toadd more robustness to the watermarking algorithm.

The second layer involved using the VC to protect the iristemplate by decomposing the original iris template into twoshares using (2,2) VC where one share is given to the useron a smart card while the other is stored in a database. Theproposed VC scheme allows the iris template to be perfectlyrestored with the same quality and size when the shares areavailable, and therefore it does not hinder the iris recogni-tion performance. To this end, an extra layer of security isprovided to the iris template because even if either of theshares in the database or the smart card is compromised, theoriginal template cannot be retrieved. Further, the integrity ofthe iris templates, in both the smart card and the database, isalso guaranteed with the use of the hash signatures. The gen-erated signature from the hash function is not only beneficialto maintain the integrity of the smart card but also it has beenused to select the embedding locations for the watermarkingalgorithm.

ACKNOWLEDGMENTSThe first author would like to thank the Ministry of HigherEducation and Scientific Research (MoHESR) in Iraq forsupporting his work.

The authors would like to thank the Chinese Academy ofSciences and the University of Beira Interior for providingthe iris databases.

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MOHAMMED A. M. ABDULLAH (M’10)received the B.Sc. and M.Sc. degrees in com-puter engineering in 2008 and 2010, respectively.He is currently pursuing the Ph.D. degree with theSchool of Electrical and Electronic Engineering,Newcastle University, U.K. He was a ResearchAssistant with the Department of Electrical Engi-neering and Electronics, University of Liverpool,U.K., in 2010. His research interests are in thefields of pattern recognition and image processing

with emphasis on iris recognition. He is a student member of the IET and theBritish Machine Vision Association.

SATNAM S. DLAY (M’15) received theB.Sc. degree (Hons.) in electrical and electronicengineering and the Ph.D. degree in very large-scale integration design from Newcastle Univer-sity, Newcastle upon Tyne, U.K., in 1979 and1984, respectively. In recognition of his majorachievements, he was appointed as the PersonalChair of Signal Processing Analysis in 2006.He has authored over 250 research papers rangingfrom biometrics and security, biomedical signal

processing, and implementation of signal processing architectures. He serveson several editorial boards and has played an active role in numerousinternational conferences in terms of serving on technical and advisorycommittees and organizing special sessions.

WAI L. WOO (M’09–SM’11) received the B.Eng.degree (Hons.) in electrical and electronics engi-neering and the Ph.D. degree in statistical signalprocessing from Newcastle University, Newcas-tle upon Tyne, U.K., in 1993 and 1998, respec-tively. He is currently a Senior Lecturer with theSchool of Electrical and Electronic Engineering,Newcastle University. He is also the Director ofOperations with the International Branch of New-castle University, Singapore. He has authored over

250 papers on the above topics on various journals and international con-ference proceedings. His current research interests include mathematicaltheory and algorithms for nonlinear signal and image processing, machinelearning for signal processing, blind source separation, multidimensionalsignal processing, and signal/image deconvolution and restoration. He is aSenior Member of the IET. He currently serves on the Editorial Board ofseveral international signal processing journals.

JONATHON A. CHAMBERS (S’83–M’90–SM’98–F’11) received the Ph.D. and D.Sc.degrees in signal processing from the ImperialCollege London, London, U.K., in 1990 and2014, respectively. He is currently the Head ofthe Communications, Sensors, Signal and Infor-mation Processing Group, School of Electricaland Electronic Engineering, Newcastle University,Newcastle upon Tyne, U.K. He has authored over400 conference and journal articles, many ofwhich

are in the IEEE journals. He is a fellow of the IET and the Royal Academyof Engineering, U.K. He has served as an Associate Editor of the IEEETRANSACTIONS ON SIGNAL PROCESSING from1997 to 1999 and 2004 to 2007,and a Senior Area Editor from 2011 to 2015.

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