Securing Iris Images with a Robust Watermarking Algorithm based on

Discrete Cosine Transform

Mohammed A. M. Abdullah

∗

, S. S. Dlay and W. L. Woo

Newcastle University, School of Electrical and Electronic Engineering, NE1 7RU, Newcastle Upon Tyne, U.K.

Keywords:

Biometric Protection, Biometric Watermarking, Discrete Cosine Transform, Iris Recognition, Template

Security.

Abstract:

With the expanded use of biometric systems, the security of biometric trait is becoming increasingly important.

When biometric images are transmitted through insecure channels or stored as a raw data, they become subject

to the risk of being stolen, faked and attacked. Hence, it is imperative that robust and reliable means of

protection are implemented. Various methods of data protection are available and digital watermarking is one

such techniques. This paper presents a new method for protecting the integrity of the iris images using a

demographic text as a watermark. The watermark text is embedded in the middle band frequency region of

the iris image by interchanging three middle band coefﬁcients pairs of the Discrete Cosine Transform (DCT).

Experimental results show that exchanging more than one pair will make middle band scheme more robust

against malicious attack along with making it resistant to image manipulation such as compression. The results

also illustrate that our watermarking algorithm does not introduce discernible decrease on iris image quality

or biometric recognition performance.

1 INTRODUCTION

The growing need for security in recent years has

resulted in the development of personal biometrics

identiﬁcation systems. Biometric is the science of es-

tablishing human identity using physical or behavior

traits. The advantages of personal identiﬁcation using

biometric features are numerous, such as fraud pre-

vention and secure access control (Vacca, 2007).

Although biometrics systems offer great beneﬁts

with respect to the traditional authentication tech-

niques, the problem of ensuring the security and in-

tegrity of the biometric data is still critical. Hence,

for a biometric system to work properly, the veriﬁer

system must guarantee that the biometric data came

from a legitimate person at the time of enrollment (Yi-

wei et al., 2002). Encryption and watermarking can

be used to achieve this purpose. However, encryption

cannot provide security after the data is decrypted. On

the contrary, watermarking involves hiding informa-

tion into the host data for protecting its integrity, so it

can provide security even after decryption.

A number of watermarking techniques are avail-

able for embedding information securely in an image.

These can be broadly classiﬁed as transformation do-

∗

Mohammed A. M. Abdullah is also a staff member

with University of Mosul and sponsored by the Ministry of

Higher Education in Iraq to complete his PhD.

main techniques (Yiwei et al., 2002; Deb et al., 2012;

Wang et al., 2009; Sakib et al., 2011) and spatial do-

main techniques (Mukherjee et al., 2004; Singh et al.,

2012). While the spatial domain techniques are hav-

ing least complexity and high payload they cannot

withstand low pass ﬁltering and common image pro-

cessing attacks (Dabas and Khanna, 2013).

Recently watermarking techniques have been used

to protect biometric templates (Islam et al., 2008;

Fouad et al., 2011; Isa and Aljareh, 2012; Majumder

et al., 2013; Paunwala and Patnaik, 2014). In (Is-

lam et al., 2008) the authors proposed a protection

algorithm for the ﬁngerprint image by watermarking

it with a password extracted from the palm print of

the same person. However, no experiments were per-

formed by the authors to show the algorithm robust-

ness against attacks. The authors in (Fouad et al.,

2011) presented a scheme for protecting the iris tem-

plate using a combination of cryptography and water-

marking. The iris image is locked with a key and em-

bedded in a cover image using combinations of Least

Signiﬁcant Bit (LSB) and Discrete Wavelet Trans-

form (DWT) as a watermarking algorithm. A second

key is used to specify the embedding locations. Nev-

ertheless, the two keys (iris key and embedding key)

are required in iris extraction process. Later, (Isa and

Aljareh, 2012) proposed a watermarking algorithm

for protecting the biometric image. Face images were

108

Abdullah M., Dlay S. and Woo W..

Securing Iris Images with a Robust Watermarking Algorithm based on Discrete Cosine Transform.

DOI: 10.5220/0005305701080114

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 108-114

ISBN: 978-989-758-091-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

watermarked using the Cox watermarking algorithm

(Cox et al., 1997). The face image acted as the user-

name for identiﬁcation while the watermark acted as

a password for authentication. However, the problem

with the Cox watermarking scheme is that the original

image is needed at the watermark detector stage.

The authors in (Majumder et al., 2013) applied

biometric watermarking by taking the DWT and the

singular value decomposition of the host image to ob-

tain an Eigen value vector. Next the iris features is ex-

tracted with DCT to obtain 200 coefﬁcients and then

embedded in the Eigen value vector derived from the

host image. Despite of the good result reported by the

authors, the drawback of this approach is that the fea-

ture extraction algorithm for iris cannot be changed.

The work in (Paunwala and Patnaik, 2014) used the

ﬁngerprint and iris features as watermark for a cover

image. The image is divided in blocks then each block

is transformed into a two-dimensional DCT and clas-

siﬁed as smoother block or edge block. The biomet-

ric features are embedded in the low frequency coef-

ﬁcients of the 8× 8 DCT blocks while the edge block

is eliminated.

Biometrics like ﬁngerprint, face, and iris conveys

unique biological information of a person. Nowadays,

iris recognition is one of the most reliable biometric

techniques that are extensively used for personal iden-

tiﬁcation. To guarantee the reliability of iris recogni-

tion, a protection method is needed. The literature

review revealed that a few research have been car-

ried out so far to enhance the security of iris biometric

through watermarking.

This paper presents a watermarking algorithm

based on exchanging 3 middle band coefﬁcients pair

of the DCT using text data as watermarks for pro-

tecting the evidentiary integrity of iris images. The

following section describes the Middle Band Coefﬁ-

cient Exchange (MBCE) algorithm. Section III ex-

plains the proposed algorithm. Experimental design

and performance analysis are given in Section IV and

Section V respectively. Finally, Section VI concludes

this paper.

2 WATERMARKING

ALGORITHM

Watermarking techniques in DCT domain allow an

image to be divided into different frequency bands,

so embedding the watermarking information in a spe-

ciﬁc frequency band becomes much easier (Langelaar

et al., 2000). Current literature survey reveals that the

middle frequency bands are most suitable for embed-

ding the watermark because the low frequency band

carries the most visual important parts of the image

while the high frequency band is exposed to removal

through compression and noise attacks on the image.

Therefore, embedding the watermark in the middle

frequency band does not affect the visual important

parts of the image (low frequency) nor overexposing

them to removal through attacks when high frequency

components are targeted (Langelaar et al., 2000).

2.1 Watermarking Algorithm

The idea of the classical Middle Band Coefﬁcient Ex-

change (MBCE) scheme was presented by (Zhao and

Koch, 1995). Later, (Hsu and Wu, 1996) applied the

DCT to implement the middle band coefﬁcients em-

bedding. The algorithm encodes one-bit of a binary

watermark object into one 8 × 8 sub-block of the host

image by ensuring that the difference of two mid-band

coefﬁcients is positive in case of the encoded value is

1. Otherwise, the two mid-band coefﬁcients are ex-

changed.

Accordingly, after the DCT is applied to the im-

age, an 8 × 8 block dimension is taken. Each DCT

block consists of three frequency bands as illustrated

in Figure 1. F

stands for the low frequency com-

ponents of the block, while F

denotes the higher

frequency components. F

is the middle frequency

band and is chosen for embedding watermark infor-

mation. This avoids signiﬁcant modiﬁcations to the

cover image while providing additional resistance to

lossy compression techniques which targets the high

frequency components (Hernandez et al., 2000).

For the frequency band FM, two locations from

the DCT block (DCT

(u1,v1)

and DCT

(u2,v2)

) are chosen

as the region for comparison. After the watermark

text is converted to a binary image, each pixel value

is checked. The coefﬁcients are swapped if the rela-

tive size of each coefﬁcient does not agree with the bit

that is to be encoded. Thus, if the pixel value in the bi-

nary text is 1, the DCT coefﬁcient are swapped such

that DCT

(u1,v1)

> DCT

(u2,v2)

. On the other hand, in

Figure 1: Frequency regions in 8×8 DCT block (Langelaar

et al., 2000).

SecuringIrisImageswithaRobustWatermarkingAlgorithmbasedonDiscreteCosineTransform

109

case of 0, they swapped so DCT

(u2,v2)

> DCT

(u1,v1)

Hence, instead of inserting any data, this scheme is

hiding watermark data by interpreting 0 or 1 with the

relative values of the two ﬁxed locations in F

re-

gion (DCT

(u1,v1)

and DCT

(u2,v2)

). Swapping of such

coefﬁcients will not alter the watermarked image sig-

niﬁcantly, due to the fact that the DCT coefﬁcients

of middle frequencies have similar magnitudes (Zhao

and Koch, 1995; Johnson and Katzenbeisser, 2000).

In the extraction stage, the 8 × 8 DCT of the image

is taken again, so it will decode a ”1” if DCT

(u1,v1)

DCT

(u2,v2)

; otherwise it will decode a ”0” to form the

watermark.

3 PROPOSED ALGORITHM

The previous scheme has a serious drawback. If only

one pair of coefﬁcient is used to hide the watermark

data, it will become vulnerable to attack as the at-

tacker can analyze some watermarked copies of an

image to predict the location of these coefﬁcients as

well as destroy them.

To solve this problem, three coefﬁcient pairs are

chosen from the F

frequency band to increase re-

dundancy and make the scheme robust against differ-

ent attacks. The numbers of the pairs to be swapped

in this paper were chosen as a trade-off between com-

plexity and performance.

Moreover, to improve the robustness of the wa-

termarking algorithm, we propose to add a wa-

termark strength constant k such that DCT

(u1,v1)

−

DCT

(u2,v2)

> k. If coefﬁcients do not meet these crite-

ria, a constant value will be added to satisfy the rela-

tion.

3.1 Strength of Watermark

The strength of watermark has been increased by

choosing an appropriate value of the proposed

strength constant k. Increasing k will degrade the im-

age but it will reduce the chance of errors at the detec-

tion phase. Experimental results indicate that setting

k equals to 15 is the most suitable value in the percep-

tibility versus robustness. Therefore, the test has been

conducted by keeping k=15.

3.2 Embedding Algorithm

Each 8× 8 block of image will be used to hide one bit

of watermark text. A binary text image (W ) is taken as

a watermarking object which can be interpreted as a

1D array of 1 and 0. The watermark text image carries

the person bio-information such as name, ID and date

Algorithm 1: Embedding algorithm.

Input: X,W

(X: host image, W : watermarking text)

Output: Y

(Y : watermarked image)

1: loop

2: X

8×8(i)

= X;

{subdivide the host image (X) into blocks of

8 × 8 pixel}

3: X

DCT (i)

= 2D-DCT (X

8×8(i)

)

{Compute the 2D-DCT of each 8 × 8 block of

the host image}

4: for i = 1 → size(W ) do

5: if W

(i)

= 0 then

6: exchange DCT coefﬁcients such that

locations at DCT (2, 5),DCT (3,5) and

DCT (4, 3) of the 8 × 8 sub-image will be

larger than the locations

DCT (1, 6),DCT (2,6) and DCT (5, 2)

respectively

{Now adjust the three values such that

their difference becomes larger than the

constant k, thus: }

7: if DCT (2, 5) − DCT (1, 6) < k then

8: DCT (2, 5)=DCT (2,5)+k/2

9: DCT (1, 6)=DCT (1,6) − k/2

10: end if

11: repeat step 7-10 for the other two

coefﬁcients: DCT (3,5) and DCT (4, 3)

12: else if W

(i)

= 1 then

13: exchange DCT coefﬁcients such that

locations at DCT (2, 5),DCT (3,5) and

DCT (4, 3) of the 8 × 8 sub-image will be

smaller than the locations

DCT (1, 6),DCT (2,6) and DCT (5, 2)

respectively

14: end if

{Now adjust the three values such that their

difference becomes larger than the constant

k, thus: }

15: if DCT (1, 6) − DCT (2, 5) < k then

16: DCT (1, 6)=DCT (1,6)+k/2

17: DCT (1, 5)=DCT (2,5) − k/2

18: end if

19: repeat step 15-18 for the other two

coefﬁcients: DCT (2,6) and DCT (5, 5)

20: Take inversre DCT to reconstruct Y

21: end for

22: end loop

of birth. The steps of the embedding algorithm are

shown in Algorithm 1 while the ﬂow chart is depicted

in Figure 2.

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110

Figure 2: The ﬂow chart of the embedding algorithm.

3.3 Detection Algorithm

Watermark extraction is the reverse procedure of wa-

termark embedding. The steps of the detection algo-

rithm are shown in Algorithm 2.

4 EXPERIMENTAL DESIGN

The proposed algorithm has been tested on session

one of UBIRIS V1 iris database. The images were

watermarked with 64 × 64 pixel text image shown in

Figure 3(d) after converting it to a binary image.

5 RESULTS AND

PERFORMANCE ANALYSIS

A robust watermarking algorithm should detect the

embedded information reliably even if the water-

marked image is degraded by different transforma-

Algorithm 2: Detection algorithm.

Input: Y

(Y : watermarked image)

Output: W

(W : binary text)

1: loop

2: Y

8×8(i)

= Y ;

{subdivide the cover image (Y ) into blocks of

8 × 8 pixel}

3: Y

DCT (i)

= 2D-DCT (Y

8×8(i)

)

{Compute the 2D-DCT of each 8 × 8 block of

the cover image}

4: if DCT (2, 5), DCT (3,5),DCT (4, 3) >

DCT (1, 6), DCT (2,6),DCT (5, 2) then

5: W (i)=1

6: else

7: W (i)=0

8: end if

9: end loop

10: reconstruct the binary text image W from W

(i)

tions. Besides robustness, a good watermarking algo-

rithm should be imperceptible to the user as well as

it should not affect the matching performance of the

biometric system badly.

In order to evaluate our method, a set of different

tests have been carried out on our proposed algorithm

as shown in the next sub-sections.

Figure 3: Perceptibility of the watermarked image; (a) Orig-

inal image, (b) Watermarked image, (c) The difference im-

age, (d) Original watermark, (e) binarized text and (f) the

extracted watermark.

SecuringIrisImageswithaRobustWatermarkingAlgorithmbasedonDiscreteCosineTransform

111

5.1 Watermark Perceptibility

The perceptibility is the similarity between the orig-

inal and the watermarked image. Thus, the water-

marked object should be imperceptible to the user.

According to Figure 3(c) the difference between the

original and the watermarked iris image is not notice-

able to the naked eye without the help of image pro-

cessing techniques.

To evaluate the performance of the watermarking

algorithm, Peck Signal to Noise Ratio (PSNR) Bit Er-

ror Rate (BER) are calculated. The average PSNR

between the original iris and the watermarked iris is

37.69% and the average BER is 0.257% while the av-

erage PSNR and BER of the extracted watermarking

text are 84.25% and 0.0244% respectively.

5.2 Effect on Matching Performance

To ﬁnd the effect of the watermarking on the iris

recognition performance, Maseks approach (Masek,

2003) for iris recognition has been implemented and

the Equal Error Rate (EER) is calculated for the non-

watermarked iris images. After that, the proposed wa-

termarking algorithm is applied to the same iris im-

ages and the EER is calculated again. Figure 4 illus-

trates the effect of watermarking of matching perfor-

mance in term of Receiver Operating Characteristics

(ROC) curve.

According to Figure 4, the proposed watermark-

ing algorithm hardly affects the EER across all

classes. Consequently, the recognition performance

will not be affected by the proposed watermarking

system.

5.3 Performance Against Compression

and Noise

Apart from analyzing the change in perceptibility and

matching performance, other considerations that may

degrade the images were tested on our algorithm.

There are several types of these degradations. For

Figure 4: Effect of the proposed watermarking algorithm on

iris recognition performance.

instance, the images are compressed when transmit-

ting large image ﬁles over low bandwidth channel. In

addition the iris images can be degraded if they are

transmitted over a noisy communication channel.

To simulate these effects, image compression al-

gorithm, Joint Photograph Expert Group (JPEG) has

been employed with different quality factors (Q). In

term of the noisy channel, we applied the Additive

White Gaussian Noise (AWGN) to the iris images

with zero mean and variance equals to 10

−3

. Even

with image compression and the added noise, the ex-

tracted text is still discernible. Figure 5 depicts the ex-

tracted watermarking texts from the manipulated iris

images.

5.4 Performance Against Image

Manipulations and Attacks

A robust watermarking algorithm should confront dif-

ferent signal processing signal processing distortions

and attacks. A number of image manipulations were

tested on our algorithm such as median ﬁltering, his-

togram equalization and salt & pepper (noise density

= 0.005). For each type of manipulation, the match-

ing performances of the watermarked iris images are

compared with the manipulated iris images in terms

of ROC curves and EER. Moreover, the BER and

PSNR of the extracted text are also calculated after

these manipulations.

Figure 5 depicts the effect of different image ma-

nipulations on the extracted text while Figure 6 illus-

trates the effect of these image manipulations on iris

Figure 5: Extracted watermarked text after different attacks.

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112

Figure 6: Effect of different manipulation on iris recogni-

tion performance using the proposed scheme.

recognition performance using the proposed method.

According to Figure 6, no series loss in recognition

performance is noticed in term of EER.

In order to appreciate the efﬁciency of the pro-

posed method, the classical MBCE scheme (Hsu and

Wu, 1996) is implemented and the proposed strength

constant (k = 15) is introduced, then the same manip-

ulations are applied to the watermarked iris images.

Table 1 summarizes the PSNR and BER of extracted

watermark text after JPEG, median ﬁlter, histogram

equalization, Gaussian noise and salt & pepper noise

using our method and the classical method.

The proposed algorithm sustained all above image

manipulations and demonstrated that the watermark-

ing scheme is resistant to different attacks.

6 DISCUSSION AND

CONCLUSIONS

This paper presents a novel scheme for image water-

marking to protect the integrity of the biometric im-

age. A binary text image which accommodates the

bio data of the person to be authenticated is embedded

in the iris image by interchanging the middle band co-

efﬁcients using DCT. Exchanging more than one pair

of the middle band coefﬁcient make the watermark-

ing scheme robust as it is impossible to the attacker to

predict the three pairs that have been used to hide the

data in each DCT block. Concurrently, the attacker

cannot disturb all the middle coefﬁcients as it will in-

ﬂuence the image badly.

Experimental results indicate that the proposed al-

gorithm is resistant to the common image manipula-

tions such as JPEG compression, ﬁltering and nois-

ing. The results also illustrate that our watermark-

ing scheme does not signiﬁcantly impede iris image

quality or biometric matching performance. Empir-

ical experiments show that swapping the pairs (2,5)

and (1,6), (3,5) and (2,6), (4,3) and (5,2) in each 8× 8

DCT block are visually imperceptible and maintain

Table 1: EER and PSNR of the extracted watermark after

different manipulations suing the clasical MBCE and the

proposed algorithm.

Mnipulation type

Proposed Method Classical MBCE

PSNR EER PSNR EER

JPEG (Q=80) 68.19 0.97% 53.35 5.1%

JPEG (Q=70) 60.13 2.3% 44.35 8.23%

Median ﬁlter 55.92 4.2% 44.35 9.11%

Histogram

equalization

73.47 0.29% 60.67 2.83%

Gaussian noise 65.81 1.65% 62.42 2.35%

Salt & pepper 67.63 1.2% 59.31 3.87%

the iris recognition performance.

The proposed watermarking scheme is beneﬁcial

to the biometric system in a number of ways. For

example, the biometric traits and the bio informa-

tion of an individual are usually stored in indepen-

dent databases. Digital watermarking integrates the

biometric trait with the personal information in a sin-

gle ﬁle and hence allows the data to be stored and

extracted at the same time. Moreover, the integrity of

the biometric trait can be veriﬁed from the extracted

text.

One of the main advantages of our watermarking

scheme is that it can be readily applied to any biomet-

ric image other than the iris image. On the other hand,

the proposed algorithm does not require the original

image for watermark extraction.

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