# Region-Based Watermarking of Biometric Images: Case Study in Fingerprint Images

Authors: K. Zebbiche; F. Khelifi
Journal: International Journal of Digital Multimedia Broadcasting
 (2008)

---

## Abstract

In this paper, a novel scheme to watermark biometric
images is proposed. It exploits the fact that biometric
images, normally, have oneregion of interest, which represents
the relevant part of information processable by most of the
biometric-based identification/authentication systems. This proposed
scheme consists of embedding the watermark into the
region of interest only; thus, preserving the hidden data from
the segmentation process that removes the useless background
and keeps the region of interest unaltered; a process which can
be used by an attacker as a cropping attack. Also, it provides
more robustness and better imperceptibility of the embedded
watermark. The proposed scheme is introduced into the optimum
watermark detection in order to improve its performance. It is
applied to fingerprint images, one of the most widely used and
studied biometric data. The watermarking is assessed in two
well-known transform domains: the discrete wavelet transform
(DWT) and the discrete Fourier transform (DFT). The results
obtained are very attractive and clearly show significant improvements
when compared to the standard technique, which
operates on the whole image. The results also reveal that the
segmentation (cropping) attack does not affect the performance
of the proposed technique, which also shows more robustness
against other common attacks.


---

## Body

## 1. Introduction

Biometric-based systems that use physiological characteristics and/or
behavioral traits offer a good alternative to traditional systems such as
token-based or knowledge-based systems. These systems are more reliable and
more user friendly. However, there are many issues that need more attention,
especially the security aspect of both biometric system and biometric data.
Several researchers show the existence of many threats and attacks that may
affect the security and the integrity of biometric-based systems [1–4]. The problems that may arise
from the attacks on such systems are raising concerns as more and more biometric
systems are deployed [5]. Some techniques such as cryptography and
watermarking
have been introduced to thwart some of these attacks. Watermarking techniques
are gaining more interest by providing promising results [6–8]. For example,
watermarking
of fingerprint images can be used to secure central databases from which
fingerprint images are transmitted on request to intelligence agencies in order
to use them for identification purposes (see Figure 1).Figure 1
Block diagram of a watermarking application for
fingerprint images.In the literature, watermarking has been introduced
and shown to be satisfying the need for the protection of digital data. It can
be used for many security purposes such as copyright protection,
fingerprinting, copy protection, data authentication, and so forth [9]. Depending on the application,
the watermarking schemes can be cast in two classes. In the first class, often
known as multibit watermarking, a specific data, such as ID or track number, is
embedded into the host data. In this case, the embedded watermark communicates
a multibit message which must be extracted accurately at the decoding side
[10, 11]. In the second class, it is
not known whether a candidate watermark is embedded in the input data. The task
here is therefore to verify the presence of the watermark, usually referred to
as watermark detection [12, 13].In these applications, the basic requirement is that
the watermark should remain in the host data, even if its quality
is degraded, intentionally or unintentionally. Examples
of unintentional degradations are applications involving storage or data
transmission where lossy compression is used; also filtering, resampling,
digital-analog (D/A), and analog-digital (A/D) conversion may affect the
quality of the image. The host data can also be intentionally attacked in order
to remove the watermark by using malicious data processing techniques such as
noise addition, cropping, rotation, and translation.The cropping technique, which consists of removing a
portion of the image, remains one of the toughest attacks to deal with. Indeed,
the attacker might apply it to take out parts of the image which are useless;
hence, a portion of the watermark embedded within these regions is easily
removable. Unfortunately, most of the watermarking algorithms are not robust
enough to such an attack. Also, the watermark algorithms that make use of the
human visual systems (HVSs) characteristics intend to maximize the inserted
watermark, especially, in the texture areas but these algorithms do not make
the difference between the useful textures and the useless noise. In order to
overcome this problem, the watermark should be inserted into the most relevant
part(s) of the image, that is,region of interest (ROI). However, this
is difficult to apply to natural images since the ROI of such images is
user-dependent or just undefined.Several biometric-based systems, such as fingerprint,
face, iris, or hand, use images as input data. A common characteristic of these
images is that they have only one ROI, constituting the part processable by the
identification/authentication algorithms. The segmentation technique is usually
used to extract the ROI. However, this technique, which is basically used as a
preprocessing step, can be used by an attacker as a special case of cropping
since it removes the background area (i.e., removes the part of the watermark
embedded in this area) while keeping ROI unchanged. The motivation is that the
idea of inserting the watermark into the ROI is applicable to biometric images
whose ROI can be extracted.In this work, we propose a new scheme to embed the
watermark into the ROI of biometric images. This is motivated by the following:
(i) securing the embedded watermark against the segmentation process and
increasing the robustness of the watermark against other attacks such as
filtering, noise because even the attacker knows that the watermark is embedded
in this region, concentrating his attacks on that area degrades significantly
its quality, hence, making it useless; (ii) providing more transparency to the
embedded watermark since the human eye is less sensitive to changes in textured
areas.Region-based method proposed in this work can be
viewed as a special case of personalization because the proposed algorithm is
adaptive and only a portion of the data (i.e., ROI) is watermarked. The
proposed scheme is applied on fingerprint images. Note that fingerprint-based
systems are regarded as the most powerful and widely deployed biometric
systems. To extract the ROI of such images, referred here to asridges area,
the segmentation technique
proposed by Wu et al. [14] is modified in order to use adaptive thresholding.
For sake of completeness, the watermark is embedded into the discrete wavelet
transform (DWT) and discrete Fourier transform (DFT), where the DWT
coefficients are statistically modeled by the generalized Gaussian distribution
(GGD) and the DFT coefficients are modeled by the Weibull distribution.
Experiments were carried out on test images from real-fingerprint database and
the results obtained clearly show the performance introduced by the proposed
scheme. Also, the robustness of inserting the watermark into the ROI is
assessed in the presence of attacks such as wavelet scalar quantization (WSQ)
compression, mean filtering and additive white Gaussian noise (AWGN).The paper is organized as follows: the proposed watermarking
scheme for biometric images is explained in Section2.
Application of the proposed scheme to fingerprint images is described in
Section 3. Experiments were carried out in Section 4 to assess the impact of
the proposed technique on the overall performance of the optimum detector.
Finally, conclusions are drawn in Section 5.


## 2. Proposed Watermarking Scheme for Biometric Images

The proposed
watermarking scheme is depicted by Figure2. At the encoder side, we aim to
insert the watermark into the ROI only and exclude the background area;
therefore, the ROI is first extracted. The extraction techniques can be either
block-wise or pixel-wise and usually provide a binary image, called region
mask, where 1 indicates that the block (or pixel) belongs to the ROI and 0
indicates that the block (or pixel) belongs to the background area. Then, the
region mask is divided into nonoverlapping blocks to obtain a watermarking
mask; where each block is classified based on the number of 1 in it. If the
number of 1 exceeds a given threshold, then the block is classified as ROI
block, otherwise, it is a background block. This watermarking mask is used to
select the blocks that will hold the watermark.Figure 2
Proposed watermarking scheme for biometric data.It is worth noting that there are two issues to be
taken into account when choosing the ROI extraction technique for watermarking
purposes, which are as follows: (i) the robustness of the technique against
possible attacks that may affect a watermarked image, that is, the ROI
extraction technique must extract approximately the same ROI from the original
and the watermarked images even after applying attacks; (ii) computational
complexity. Indeed, the block-wise extraction scheme is less complex than the
pixel-wise one. However, this comes at the cost of accuracy. From the view
point of watermarking, pixel-wise extraction techniques are more powerful since
they provide more accuracy of ROI at the detector side. This is obviously
required in blind watermarking. The proposed watermarking scheme is equipped
with an optimum watermark detector. In such a case, the false-alarm probability
(P
fa) and the detection probability (P
det) are the natural performance measures.


## 3. Application to Fingerprint Images

The region-based
method proposed in this paper can be viewed as a special case of personalization
because the algorithm adaptively operates on a portion of the input data (i.e.,
ROI) as illustrated by Figure3. As can be seen, the encoding system uses the
ROI to insert the watermark and keeps the background image unchanged. The
bigger the ROI, the larger the number of coefficients that can be used for
watermarking. Once the watermark is embedded, the background area is used to
reconstruct the watermarked image. At detection, the detector follows the same
steps to extract the ROI and check the presence of the watermark. It is worth
mentioning that the selected extraction method is first assessed on the
original images by varying the attacks strength. This method should be robust
enough to attacks that might alter the watermarked image. Although the
watermarked image may undergo attacks that aim to remove the watermark, the
visual quality should be kept useful so that the attacker can use it. We have
carried out experiments on the original images to verify the efficiency of the
extraction method against different attacks with various strengths controlled
by a number of parameters such as compression ratio, noise variance, filtering
window size.Figure 3
Personalized watermarking system applied to
fingerprint images.


### 3.1. Region of Interest Extraction

A fingerprint
is a pattern of alternating convex skin calledridges and concave skin
called valleys with a spiral-curve-like line shape. In fingerprint
images, the ridges area is considered as the ROI and the noisy area around it
and at the borders is the background area. In the literature, several methods
have been proposed to extract the ROI from fingerprint images. These methods
can be divided into two categories: block-wise and pixel-wise features
classification. The algorithms that fall in the first category decompose the
image into blocks. Then, some characterizing features, such as the local
histogram of ridge orientation, gray-level variance, magnitude of the gradient,
are calculated and based on these features, a classifier can be used to decide
whether a block belongs to the ROI or to the background area. In the second
category, pixel features are first extracted. This includes for example
coherence, average gray level, variance and Gabor response, and then a simple
classifier is chosen for classification. Such pixel-wise methods provide
accurate results, but their computational complexity is higher than the
commonly used block-wise methods.In this work, Harris corner point features method
[14] is adopted to
extract the ridges area of fingerprint images. The Harris corner detector is based
on the local autocorrelation function of a signal; where the local
autocorrelation function measures the local changes of the signal with patches
shifted by a small amount in different directions [15]. Wu et
al. found in
[14] that the strength
of the Harris point in the ridges area is much higher than that of the
background area. However, the authors used different thresholds, which are
determined experimentally for each image. Also, they reported the existence of
some noisy regions in the background area corresponding to high strength
values, which cannot be eliminated even by using high threshold values and
proposed to use a heuristic algorithm based on the corresponding Gabor response
in order to discard these noisy regions.To make this technique more flexible and practical, it
has been modified by using the Otsu thresholding method [16] to adaptively determine
adequate thresholds. Otsu method is based on maximizing the between-class
variance to find the optimum threshold. This modification provides an excellent
threshold for fingerprint images with different visual qualities. To eliminate
the noisy regions, some morphological methods are then applied, leading to
excellent segmented images.The Harris point is a pixel-wise method, the
segmentation mask (Figure4(b)) has the same size as the original image and it
is partitioned to obtain the watermarking mask (Figure 4(c)). Note that in this
paper, only blocks whose all pixels belong to the ridges area are taken into
account, that is, 100% of the pixels belong to the ridges area for
every selected block.Example of fingerprint image: (a) original image, (b) region mask, (c)
watermarking mask. The block size=
32.
(a)
(b)
(c)


### 3.2. Watermark Embedding

The watermark
is embedded into the transform domain. In this paper, we consider two widely
used transforms: the DWT and the DFT. These transforms can be applied to the
entire image or in a block-wise manner. Also, the multiplicative rule is used
to embed the watermark due to its advantages over the additive one, especially
in exploiting the HVS characteristics. The watermark, denoted byw
=
{
w
1
,
w
2
,
…
,
w
N
},
is a pseudorandom sequence uniformly distributed in [
−
1,
+
1
] and generated by using a secret key K.
The embedding process is comprised of the steps described below.(i)
Extract the ROI for the input imageI and obtain the region mask RM.
(ii)
Determine the watermarking maskWM from the ROI by decomposing RM into nonoverlapping blocks of size m
×
m.
(iii)
Decompose the imageI into nonoverlapping blocks B
i
j of size m
×
m pixels and only the blocks that belong to the
ROI are selected to carry the watermark, that is, if W
M
i
j
=
1 the corresponding block B
i
j is selected; otherwise, it remains unchanged.
(iv)
Transform the selected blocks using a
transform, such as DWT and DFT, to obtain the original coefficientsx
=
{
x
1
,
x
2
,
…
,
x
N
}.
The watermark is embedded into the original image using the multiplicative rule
as follows:(1)
y
i
=
(
1
+
λ
w
i
)
x
i
,where y
=
{
y
1
,
y
2
,
…
,
y
N
} represents the watermarked coefficients and λ is the strength of the watermark.


### 3.3. Watermark Detection

The goal of
the optimum watermark detector is to verify whether or not there is a candidate
watermark embedded in the received image, based on its statistical properties.
This problem is usually formulated as a binary hypothesis test, in which, two
hypotheses are used to represent the presence/absence of a given watermark
within the host data. The two hypotheses can be established as follows:H
0:
the coefficients y are not watermarked by the candidate watermark w
∗;
H
1:
the coefficients y are watermarked by the candidate watermark w
∗.
The decision
rule for the binary test formulated above, denoted by Λ
(
y
),
relies on maximum-likelihood method based on Bayes' decision theory. The
likelihood ratio can be written as(2)
Λ
(
y
)
=
f
y
(
y
|
H
1
)
f
y
(
y
|
H
0
)
,where f
y
(
y
|
H
1
) and f
y
(
y
|
H
0
) represent the probability distribution
function (pdf) of vector y conditioned to the hypotheses H
1 and H
0,
respectively. Following the same steps as described by Barni et al.  in
[12], the decision
rule is defined as(3)
l
(
y
)
=
∑
i
=
1
N
[
ln
⁡
(
f
x
i
(
y
i
1
+
λ
w
i
∗
)
)
−
ln
⁡
(
f
x
i
(
y
i
)
)
]
≷
H
0
H
1
η
′
,where l
(
y
)
=
ln
⁡
(
Λ
(
y
)
).
The decision rule reveals that H
1 is accepted (i.e., the coefficients y are marked by the sequence w
∗) only if l
(
y
) exceeds the threshold η
′.
By employing the Neyman-Pearson criterion [17], the threshold is obtained in such a way that the
detection probability P
det is maximized, subject to a fixed false-alarm
probability P
fa [12]:(4)
η
′
=
erf
c
−
1
(
2
P
fa
)
2
σ
0
2
+
μ
0
,where erfc
(
⋅
) is the complementary error function, μ
0
=
E
[
l
(
y
)
|
H
0
] and σ
0
2
=
V
[
l
(
y
)
|
H
0
] are the mean and the variance of l
(
y
) under hypothesis H
0,
respectively.


#### 3.3.1. Optimum Watermark Detector Structure Based on the GGD

To describe the
probability characteristics of DWT coefficients, the GGD is widely used in the
literature and some studies show that this distribution provides the closest
approximation [18].
The GGD pdf of zero-mean is given by(5)
f
X
(
x
;
α
,
β
)
=
β
2
α
Γ
(
1
/
β
)
exp
⁡
(
−
(
|
x
|
α
)
β
)
,where Γ
(
⋅
) is the Gamma function, Γ
(
z
)
=
∫
0
∞
e
−
t
t
z
−
1
d
t
,
z
>
0.
The parameter α is referred to as the scale parameter and it
models the width of the pdf peak (standard deviation) and β is called the shape parameter and it is
inversely proportional to the decreasing rate of the peak.By substituting (5) in (3), the log-likelihood for the
GGD is given by [19](6)
l
(
y
)
=
∑
i
=
1
N
(
|
y
i
|
α
)
β
[
1
−
|
1
+
λ
w
i
∗
|
−
β
]
,where α and β are the parameters of the GGD for the
coefficients y.The thresholdη
′ can be obtained by using (4), where μ
0 and σ
0
2 are given by(7)
μ
0
=
∑
i
=
1
N
1
β
[
1
−
|
1
+
λ
w
i
∗
|
−
β
]
,
σ
0
2
=
∑
i
=
1
N
1
β
[
1
−
|
1
+
λ
w
i
∗
|
−
β
]
2
.The parameters α and β can be estimated as described in [20].


#### 3.3.2. Optimum Detector Structure Based on the Weibull Model

The DFT
coefficients are widely modeled by the Weibull distribution in the literature
[12, 21]. Its pdf is defined as(8)
f
X
(
x
;
α
,
β
)
=
β
α
(
x
α
)
β
−
1
exp
⁡
[
−
(
x
α
)
β
]
,
x
≥
0,where β
>
0 represents the shape parameter and α
>
0 is the scale parameter of the distribution.
The detector structure for the Weibull distribution is defined by Barni et al. [12] and given
by(9)
l
(
y
)
=
∑
i
=
1
N
y
i
β
(
(
1
+
λ
w
i
∗
)
β
−
1
α
β
(
1
+
λ
w
i
∗
)
β
)
,where α
i and β
i are the parameters of the Weibull model for
the coefficients y.Equation (4) is used to derive the threshold η
′ where the mean μ
0 and the variance σ
0
2 are defined as(10)
μ
0
=
∑
i
=
1
N
(
(
1
+
λ
w
i
∗
)
β
−
1
(
1
+
λ
w
i
∗
)
β
)
,
σ
0
2
=
∑
i
=
1
N
(
(
1
+
λ
w
i
∗
)
β
−
1
(
1
+
λ
w
i
∗
)
β
)
2
.


## 3.1. Region of Interest Extraction

A fingerprint
is a pattern of alternating convex skin calledridges and concave skin
called valleys with a spiral-curve-like line shape. In fingerprint
images, the ridges area is considered as the ROI and the noisy area around it
and at the borders is the background area. In the literature, several methods
have been proposed to extract the ROI from fingerprint images. These methods
can be divided into two categories: block-wise and pixel-wise features
classification. The algorithms that fall in the first category decompose the
image into blocks. Then, some characterizing features, such as the local
histogram of ridge orientation, gray-level variance, magnitude of the gradient,
are calculated and based on these features, a classifier can be used to decide
whether a block belongs to the ROI or to the background area. In the second
category, pixel features are first extracted. This includes for example
coherence, average gray level, variance and Gabor response, and then a simple
classifier is chosen for classification. Such pixel-wise methods provide
accurate results, but their computational complexity is higher than the
commonly used block-wise methods.In this work, Harris corner point features method
[14] is adopted to
extract the ridges area of fingerprint images. The Harris corner detector is based
on the local autocorrelation function of a signal; where the local
autocorrelation function measures the local changes of the signal with patches
shifted by a small amount in different directions [15]. Wu et
al. found in
[14] that the strength
of the Harris point in the ridges area is much higher than that of the
background area. However, the authors used different thresholds, which are
determined experimentally for each image. Also, they reported the existence of
some noisy regions in the background area corresponding to high strength
values, which cannot be eliminated even by using high threshold values and
proposed to use a heuristic algorithm based on the corresponding Gabor response
in order to discard these noisy regions.To make this technique more flexible and practical, it
has been modified by using the Otsu thresholding method [16] to adaptively determine
adequate thresholds. Otsu method is based on maximizing the between-class
variance to find the optimum threshold. This modification provides an excellent
threshold for fingerprint images with different visual qualities. To eliminate
the noisy regions, some morphological methods are then applied, leading to
excellent segmented images.The Harris point is a pixel-wise method, the
segmentation mask (Figure4(b)) has the same size as the original image and it
is partitioned to obtain the watermarking mask (Figure 4(c)). Note that in this
paper, only blocks whose all pixels belong to the ridges area are taken into
account, that is, 100% of the pixels belong to the ridges area for
every selected block.Example of fingerprint image: (a) original image, (b) region mask, (c)
watermarking mask. The block size=
32.
(a)
(b)
(c)


## 3.2. Watermark Embedding

The watermark
is embedded into the transform domain. In this paper, we consider two widely
used transforms: the DWT and the DFT. These transforms can be applied to the
entire image or in a block-wise manner. Also, the multiplicative rule is used
to embed the watermark due to its advantages over the additive one, especially
in exploiting the HVS characteristics. The watermark, denoted byw
=
{
w
1
,
w
2
,
…
,
w
N
},
is a pseudorandom sequence uniformly distributed in [
−
1,
+
1
] and generated by using a secret key K.
The embedding process is comprised of the steps described below.(i)
Extract the ROI for the input imageI and obtain the region mask RM.
(ii)
Determine the watermarking maskWM from the ROI by decomposing RM into nonoverlapping blocks of size m
×
m.
(iii)
Decompose the imageI into nonoverlapping blocks B
i
j of size m
×
m pixels and only the blocks that belong to the
ROI are selected to carry the watermark, that is, if W
M
i
j
=
1 the corresponding block B
i
j is selected; otherwise, it remains unchanged.
(iv)
Transform the selected blocks using a
transform, such as DWT and DFT, to obtain the original coefficientsx
=
{
x
1
,
x
2
,
…
,
x
N
}.
The watermark is embedded into the original image using the multiplicative rule
as follows:(1)
y
i
=
(
1
+
λ
w
i
)
x
i
,where y
=
{
y
1
,
y
2
,
…
,
y
N
} represents the watermarked coefficients and λ is the strength of the watermark.


## 3.3. Watermark Detection

The goal of
the optimum watermark detector is to verify whether or not there is a candidate
watermark embedded in the received image, based on its statistical properties.
This problem is usually formulated as a binary hypothesis test, in which, two
hypotheses are used to represent the presence/absence of a given watermark
within the host data. The two hypotheses can be established as follows:H
0:
the coefficients y are not watermarked by the candidate watermark w
∗;
H
1:
the coefficients y are watermarked by the candidate watermark w
∗.
The decision
rule for the binary test formulated above, denoted by Λ
(
y
),
relies on maximum-likelihood method based on Bayes' decision theory. The
likelihood ratio can be written as(2)
Λ
(
y
)
=
f
y
(
y
|
H
1
)
f
y
(
y
|
H
0
)
,where f
y
(
y
|
H
1
) and f
y
(
y
|
H
0
) represent the probability distribution
function (pdf) of vector y conditioned to the hypotheses H
1 and H
0,
respectively. Following the same steps as described by Barni et al.  in
[12], the decision
rule is defined as(3)
l
(
y
)
=
∑
i
=
1
N
[
ln
⁡
(
f
x
i
(
y
i
1
+
λ
w
i
∗
)
)
−
ln
⁡
(
f
x
i
(
y
i
)
)
]
≷
H
0
H
1
η
′
,where l
(
y
)
=
ln
⁡
(
Λ
(
y
)
).
The decision rule reveals that H
1 is accepted (i.e., the coefficients y are marked by the sequence w
∗) only if l
(
y
) exceeds the threshold η
′.
By employing the Neyman-Pearson criterion [17], the threshold is obtained in such a way that the
detection probability P
det is maximized, subject to a fixed false-alarm
probability P
fa [12]:(4)
η
′
=
erf
c
−
1
(
2
P
fa
)
2
σ
0
2
+
μ
0
,where erfc
(
⋅
) is the complementary error function, μ
0
=
E
[
l
(
y
)
|
H
0
] and σ
0
2
=
V
[
l
(
y
)
|
H
0
] are the mean and the variance of l
(
y
) under hypothesis H
0,
respectively.


### 3.3.1. Optimum Watermark Detector Structure Based on the GGD

To describe the
probability characteristics of DWT coefficients, the GGD is widely used in the
literature and some studies show that this distribution provides the closest
approximation [18].
The GGD pdf of zero-mean is given by(5)
f
X
(
x
;
α
,
β
)
=
β
2
α
Γ
(
1
/
β
)
exp
⁡
(
−
(
|
x
|
α
)
β
)
,where Γ
(
⋅
) is the Gamma function, Γ
(
z
)
=
∫
0
∞
e
−
t
t
z
−
1
d
t
,
z
>
0.
The parameter α is referred to as the scale parameter and it
models the width of the pdf peak (standard deviation) and β is called the shape parameter and it is
inversely proportional to the decreasing rate of the peak.By substituting (5) in (3), the log-likelihood for the
GGD is given by [19](6)
l
(
y
)
=
∑
i
=
1
N
(
|
y
i
|
α
)
β
[
1
−
|
1
+
λ
w
i
∗
|
−
β
]
,where α and β are the parameters of the GGD for the
coefficients y.The thresholdη
′ can be obtained by using (4), where μ
0 and σ
0
2 are given by(7)
μ
0
=
∑
i
=
1
N
1
β
[
1
−
|
1
+
λ
w
i
∗
|
−
β
]
,
σ
0
2
=
∑
i
=
1
N
1
β
[
1
−
|
1
+
λ
w
i
∗
|
−
β
]
2
.The parameters α and β can be estimated as described in [20].


### 3.3.2. Optimum Detector Structure Based on the Weibull Model

The DFT
coefficients are widely modeled by the Weibull distribution in the literature
[12, 21]. Its pdf is defined as(8)
f
X
(
x
;
α
,
β
)
=
β
α
(
x
α
)
β
−
1
exp
⁡
[
−
(
x
α
)
β
]
,
x
≥
0,where β
>
0 represents the shape parameter and α
>
0 is the scale parameter of the distribution.
The detector structure for the Weibull distribution is defined by Barni et al. [12] and given
by(9)
l
(
y
)
=
∑
i
=
1
N
y
i
β
(
(
1
+
λ
w
i
∗
)
β
−
1
α
β
(
1
+
λ
w
i
∗
)
β
)
,where α
i and β
i are the parameters of the Weibull model for
the coefficients y.Equation (4) is used to derive the threshold η
′ where the mean μ
0 and the variance σ
0
2 are defined as(10)
μ
0
=
∑
i
=
1
N
(
(
1
+
λ
w
i
∗
)
β
−
1
(
1
+
λ
w
i
∗
)
β
)
,
σ
0
2
=
∑
i
=
1
N
(
(
1
+
λ
w
i
∗
)
β
−
1
(
1
+
λ
w
i
∗
)
β
)
2
.


## 3.3.1. Optimum Watermark Detector Structure Based on the GGD

To describe the
probability characteristics of DWT coefficients, the GGD is widely used in the
literature and some studies show that this distribution provides the closest
approximation [18].
The GGD pdf of zero-mean is given by(5)
f
X
(
x
;
α
,
β
)
=
β
2
α
Γ
(
1
/
β
)
exp
⁡
(
−
(
|
x
|
α
)
β
)
,where Γ
(
⋅
) is the Gamma function, Γ
(
z
)
=
∫
0
∞
e
−
t
t
z
−
1
d
t
,
z
>
0.
The parameter α is referred to as the scale parameter and it
models the width of the pdf peak (standard deviation) and β is called the shape parameter and it is
inversely proportional to the decreasing rate of the peak.By substituting (5) in (3), the log-likelihood for the
GGD is given by [19](6)
l
(
y
)
=
∑
i
=
1
N
(
|
y
i
|
α
)
β
[
1
−
|
1
+
λ
w
i
∗
|
−
β
]
,where α and β are the parameters of the GGD for the
coefficients y.The thresholdη
′ can be obtained by using (4), where μ
0 and σ
0
2 are given by(7)
μ
0
=
∑
i
=
1
N
1
β
[
1
−
|
1
+
λ
w
i
∗
|
−
β
]
,
σ
0
2
=
∑
i
=
1
N
1
β
[
1
−
|
1
+
λ
w
i
∗
|
−
β
]
2
.The parameters α and β can be estimated as described in [20].


## 3.3.2. Optimum Detector Structure Based on the Weibull Model

The DFT
coefficients are widely modeled by the Weibull distribution in the literature
[12, 21]. Its pdf is defined as(8)
f
X
(
x
;
α
,
β
)
=
β
α
(
x
α
)
β
−
1
exp
⁡
[
−
(
x
α
)
β
]
,
x
≥
0,where β
>
0 represents the shape parameter and α
>
0 is the scale parameter of the distribution.
The detector structure for the Weibull distribution is defined by Barni et al. [12] and given
by(9)
l
(
y
)
=
∑
i
=
1
N
y
i
β
(
(
1
+
λ
w
i
∗
)
β
−
1
α
β
(
1
+
λ
w
i
∗
)
β
)
,where α
i and β
i are the parameters of the Weibull model for
the coefficients y.Equation (4) is used to derive the threshold η
′ where the mean μ
0 and the variance σ
0
2 are defined as(10)
μ
0
=
∑
i
=
1
N
(
(
1
+
λ
w
i
∗
)
β
−
1
(
1
+
λ
w
i
∗
)
β
)
,
σ
0
2
=
∑
i
=
1
N
(
(
1
+
λ
w
i
∗
)
β
−
1
(
1
+
λ
w
i
∗
)
β
)
2
.


## 4. Experimental Results

In order to efficiently measure the actual
performance of proposed technique, experiments were carried out on real
fingerprint images of size448
×
478 taken from Fingerprint Verification
Competition “FVC 2000, DB3” database [22]. These images have been chosen with respect to their
different visual quality (Figure 5). The performance of the proposed technique,
which embeds the watermark in the ridges area only, is compared against the
standard technique which inserts the watermark into the whole image. In the DWT
domain, Daubechies 9/7 wavelet is used. Note that such a wavelet has been
adopted by the FBI as part of the wavelet scalar quantization (WSQ) compression
standard for fingerprint images. The watermark is embedded in all coefficients
in the third level subbands, except the approximation subband. An approach
similar to that proposed in [12] is used to cast the watermark in the DFT domain,
where the watermark is inserted into the magnitude of a set of full-frame
coefficients. Blind detection is adopted for all experiments, that is, the statistical
model parameters are directly estimated from the watermarked data. The receiver
operating characteristics (ROCs) curves are used to assess the performance of
both the proposed and the standard techniques. The ROC curves represent the
variation of the detection probability (P
det) against the false-alarm probability (P
FA). Note that for our proposed technique, the
number of coefficients to be watermarked (the length of the watermark sequence)
is image dependent. The larger the ROI (i.e., ridges area), the higher the
number of coefficients to be watermarked (the length of the watermark) and vice
versa. For the size of the blocks m used to determine the watermarking mask, it
has been set to 32 after extensive experiments held on many fingerprint
images. This value allows the extraction of the ridges area even after applying
severe attacks.Test images with different visual quality from DB3:
(a) Image 22_6, (b) Image 88_1, (c) Image 46_2, and (d) Image 24_3.
(a)
(b)
(c)
(d)At the first stage, we investigate the
performance of the proposed technique against the standard one without the
presence of any attack. The probability of false alarm is varied in the range10
−
5 to 10
−
1 and the value of the strength λ is fixed to value 0.10. The experimental ROC
curves are computed by measuring the performance of the actual watermark
detection system by calculating the probability of detection from
real-watermarked images. Experiments are then conducted by comparing the
likelihood ratio with the corresponding threshold for each value of the
false-alarm probability and for 1000 randomly generated watermark sequences.
The results obtained for the DWT domain are plotted in
Figure 6 and those
obtained for the DFT domain are plotted in Figure 7.ROC curves of test images. Watermarking applied in the
DWT domain. Strengthλ
=
0.10.
(a)
(b)
(c)
(d)ROC curves of test images. Watermarking applied in the
DFT domain. Strengthλ
=
0.10.
(a)
(b)
(c)
(d)As can be seen from Figures6 and
7, the proposed
technique outperforms the standard one even without applying any attack. This
is justified by the fact that the transform coefficients are better suited to
watermarking for the proposed technique since they correspond to a highly
textured area (i.e., ridges area) only. These coefficients allow the embedding
of strong watermarks.As mentioned earlier, an attacker may use segmentation
techniques on biometric images to remove a part of the watermark embedded
within the background area without altering the ROI. To illustrate this, the
spatial repartition of the watermark is plotted in Figure8(a:1) for the DWT
domain and in Figure 8(b:1) for the DFT domain in the case of a standard
watermarking; it represents the difference between the watermarked image and
the original one. The part of the watermark removed by the segmentation
technique is plotted in Figure 8(a:2) for the DWT domain and in
Figure 8(b:2) for the DFT domain. It represents the difference image without the ridges
area. For the sake of illustration, only the results for one image is shown
since the results for other images are very similar. As can be seen, an
important part of the watermark is embedded into the background area, which can
be removed easily by applying segmentation. Comparing
Figures 8(a:1) and 8(b:1), the watermark energy in the DWT domain is concentrated into the ridges
area (i.e., textured area). However, in the DFT domain, the watermark energy is
uniformly spread all over the image. Thus, a severe degradation of the standard
detector performance in the DFT domain is expected when applying the
segmentation attack, compared to the DWT domain.Standard watermarking of test image. Image 22_6:
(a:1): difference image between original and watermarked images in the DWT
domain; (a:2): difference image when removing ROI in the DWT domain; (b:1):
difference image between original and watermarked images in the DFT domain;
(b:2): difference image when removing ROI in the DFT domain.
(a)
(b)After applying the segmentation process on
watermarked images, the previous experiment has been carried out and the
results obtained are plotted in Figure9 for the DWT domain and
Figure 10 for
the DFT domain. For the proposed technique, the ROC curves are exactly the same
as for the first experiment, thus, the segmentation process has no influence on
the performance of the optimum detector. For the standard technique, the
probability of detection decreases significantly and the segmentation process
causes a deterioration of detection performance in both DWT and DFT domains. As
expected for the DFT domain, the degradation in performance is more significant
than that obtained in  the DWT domain.ROC curves of segmented, watermarked images.
Watermarking applied in the DWT domain. Strengthλ
=
0.10.
(a)
(b)
(c)
(d)ROC curves of segmented, watermarked images.
Watermarking applied in the DFT domain. Strengthλ
=
0.10.
(a)
(b)
(c)
(d)The performance of
the proposed technique against common attacks, namely, mean filtering, WSQ
compression, and additive white Gaussian noise (AWGN), is also evaluated. Each
attack has been applied several times with different strength values. For each
attack, the response of the detector to the embedded watermark is plotted along
with the threshold. In this way, the influence of each attack strength on the
detector response and the corresponding threshold is assessed. The theoreticalP
FA,
which is used to determine the decision threshold, has been fixed at 10
−
7 and the strength λ is set in such a way to obtain a peak
signal-to-noise ratio (PSNR) value ≈
40 for all test images and in both DWT and DFT
domains. Only results for one image are plotted since results obtained from
other images are very similar.Robustness against WSQ compression is assessed by
iteratively applying the WSQ compression on the watermarked images using the
WSQ viewer [23] and
varying the bit-rate value measured by bits per pixel (bpp). The results
obtained are reported in Figure 11. Obviously, the watermarking in the DWT
domain is more robust for both the proposed and the standard techniques since
the compression technique is operating in the same domain. On the contrary, the
watermarks embedded in the DFT domain do not resist the WSQ compression. Again,
the proposed technique outperforms the standard one.Robustness against WSQ compression. Top: DWT domain.
Bottom: DFT domain. Left side graphs: Proposed technique. Right side graphs:
Standard technique.
(a)
(b)
(c)
(d)The results of degradations due to AWGN are shown in
Figure12. The watermarked images were corrupted by AWGN with different value
of signal-to-noise ratio (SNR). For all images and in both the DWT and the DFT
domains, the watermarks are very robust for both the proposed and the standard
techniques.Robustness against additive white Gaussian noise. Top:
DWT domain. Bottom: DFT domain. Left side graphs: Proposed technique. Right
side graphs: Standard technique.
(a)
(b)
(c)
(d)Figure13 shows the results of watermarked fingerprint
images corrupted by mean filtering. The watermarked images were blurred with
different filter window size. Although the proposed technique is slightly
better than the standard one, the mean filtering affects significantly the
detector performance. Note that the detector for the standard technique in the
DFT domain is unable to detect the embedded watermarks for all images and all
filter window sizes. This is justified by the fact that this type of filtering
smooths the image and attenuates the shape of edges and textures.Robustness against mean filtering. Top: DWT domain.
Bottom: DFT domain. Left side graphs: Proposed technique. Right side graphs:
Standard technique.
(a)
(b)
(c)
(d)


## 5. Conclusions

In this paper,
a novel scheme has been proposed to watermark biometric images. This scheme
exploits the fact that biometric images have only oneregion of interest,
which constitutes the useful and unique processed region by most of the
biometric-based identification/authentication systems. This fact can also be
exploited by watermarking techniques where the watermark should be embedded
into the region of interest only, instead of spreading it into the whole image.
This proposed scheme is motivated by the following: (i) increasing the
robustness of the watermark against segmentation and other attacks such as
filtering, noise because even the attacker knows that the watermark is embedded
in this region, concentrating his attacks on that area degrades significantly
its quality, hence, making it useless; (ii) providing more transparency to the
embedded watermark since the region of interest is a highly textured area and
the human eye is less sensitive to changes in that area. The embedding process
for the proposed scheme starts by extracting the region of interest and then
embeds the watermark in this area only. This scheme is applied to fingerprint images
that are used by one of the most employed and widely deployed biometric
systems. To extract the ROI of such images, known as ridges area, we
modified the segmentation technique proposed by Wu et al. [14].The proposed scheme is used with the classical
optimum, multiplicative watermark detection. For sake of generality, the
watermark is applied to the DWT and the DFT domains. The DWT coefficients
modeled by the generalized Gaussian distribution, whereas, the DFT coefficients
are modeled by the Weibull model. The influence introduced by the proposed
scheme on the optimum detectors were assessed through experiments, carried out
on real fingerprint images with different characteristics. The results obtained
clearly show that the detector performance has been improved compared to the
standard technique, which operates on the whole image, and this even in the
absence of attacks. In addition, the segmentation technique, which has been
applied as a special case of cropping attack, affects the performance of the
standard technique since it removes the part of the watermark embedded within
the background area. However, this attack has no effect on the proposed
technique. Furthermore, the watermarks embedded using the proposed scheme show
to be more robust against some other common attacks such as WSQ compression,
mean filtering, and white noise addition.



---

*Source: 492942-2008-08-24.xml*