Journal of Information Security, 2011, 2, 91-98
doi:10.4236/jis.2011.22009 Published Online April 2011 (
Copyright © 2011 SciRes. JIS
Secur e Spr ead-Spectrum Watermarking for Telemedicine
Basant Kumar1, Harsh Vikram Singh2, Surya Pal Singh3, Anand Mohan3
1Motilal Nehru Nationa l Institute of Technology, Allahabad, India
2Kamla Nehru Institute of Technology, Sultanpur, India
3Institute of Technology, Banaras Hindu University, Varanasi, India
Received December 5, 2010; revised January 10, 2011; accepted April 12, 2011
This paper presents a secure spread-spectrum watermarking algorithm for digital images in discrete wavelet
transform (DWT) domain. The algorithm is applied for embedding watermarks like patient identification/
source identification or doctors signature in binary image format into host digital radiological image for
potential telemedicine applications. Performance of the algorithm is analysed by varying the gain factor,
subband decomposition levels, size of watermark, wavelet filters and medical image modalities. Simulation
results show that the proposed method achieves higher security and robustness against various attacks.
Keywords: Watermarking, Spread-Spectrum, Discrete Wavelet Transform, Telemedicine
1. Introduction
In recent years image watermarking has become an im-
portant research area in data security, confidentiality and
image integrity. Despite the broad literature on various
application fields, little work has been done towards the
exploitation of health-oriented perspectives of water-
marking [1-7]. While the recent advances in information
and communication technologies provide new means to
access, handle and move medical information, they also
compromise their security against illegal access and ma-
nipulation. Sensitive nature of patien t’s personal medical
data necessitates measures for medical confidentiality
protection against unauthorized access. Source authentic-
cation and data in tegrity are also important matters relat-
ing to health data management and distribution. Data
hiding and watermarking techniques can play important
role in the field of telemedicine by addressing a range
issues relevant to health data management systems, such
as medical confidentiality protection, patient and exami-
nation related information hiding, access and data integ-
rity control, and information retrieval. Medical image
watermarking requires extreme care when embedding
additional data within the medical images because the
additional information must not affect the image quality.
Security requirements of medical information, derived
from strict ethics and legal obligations imposed three
mandatory characteristics: confidentiality, reliability and
availability [8]. Confidentiality means that only author-
ized users have access to the information. Reliability has
two aspects; 1) Integrity: the information has not been
modified by non-authorized people, and 2) Authentica-
tion: a proof that the information belongs indeed to the
correct source. Availability is the ability of an informa-
tion system to be used by entitled users in the normal
scheduled conditions of access and exercise. Authentica-
tion, integration and confidentiality are the most impor-
tant issues concerned with EPR (Electronic Patient Re-
cord) data exchange through open channels [1,5]. All
these requirements can be fulfilled using suitable water-
marks. General watermarking method needs to keep the
three factors (capacity, imperceptibility and robustness)
reasonably very high [9]. Robustness is the ability to
recover the data in spite of the attacks in the marked im-
age, imperceptibility is the invisibility of the watermark
and capacity is the amount of data that can be embedded.
These requirements are hindering each other. There must
be some trade off among these requirements according to
the applications. Two common approaches of informa-
tion hiding using image covers are spatial domain hiding
and transform (frequency) domain hiding. Spatial do-
main techniques perform data embedding by directly
manipulating the pixel values, code values or bit stream
of the host image signal and they are computationally
simple and straightforward. LSB substitution, patchwork,
and spread spectrum image steganography are some of
Copyright © 2011 SciRes. JIS
the important spatial domain techniques [10,11]. In
transform domain hiding, data are embedded by modu-
lating coefficients in transform domain, such as DFT
(Discrete Fourier Transform), DCT (Discrete Cosine
Transform) and DWT (Discrete Wavelet Transform).
Transform techniques can offer a higher degree of ro-
bustness to common image processing operations, com-
pared to spatial domain techniques. Wavelet do main wa-
termarking has recently received considerable attention
due to its ability to provide both spatial and frequency
resolution [12-14]. Many wavelet based watermarking
schemes were proposed for medical images [15-18].
Watermarking technique can be further classified into
two categories, reversible and irreversible [19,20]. The
main idea behind reversible watermarking is to avoid
irreversible distortion in original image (the host image),
by developing techniques that can extract the original
image exactly. Medical image watermarking is one of the
most important fields that need such techniques where
distortion may cause wrong diagnosis. The strict specifi-
cations regarding th e quality of medical images could be
met by reversible watermarking, which allows the re-
covery of the original image without any loss of infor-
mation. Medical iden tity theft has been a seriou s security
concern in telemedicine [21]. This demands development
of secure watermarking schemes. Digital watermarking
studies have always been driven by the improvement of
robustness. On the contrary, security has received little
attention in the watermarking community. The first dif-
ficulty is that security and robustness are neighboring
concepts, which are hardly perceived as different. Secu-
rity deals with intentional attacks whereas robustness is
observed as degradation in data fidelity due to common
signal processing operations. Digital watermarking may
not be secure despite its robustness [22,23]. Therefore,
security of the watermark becomes a critical issue in
many applications. The problem of watermark security
can be solved using spread-spectrum scheme [24-27].
Spread-spectrum is a military communication scheme
invented during World War II [28]. It was designed to be
good at combating interference due to jamming, hiding a
signal by transmitting it at low power, and achieving
secrecy. These properties make spread-spectrum very
popular in present-day digital watermarking.
This paper proposes a new secure spread-spectrum
based watermarking algorithm for embedding sensitive
medical information like physician’s signature/identify-
cation code or patient identity code into radiological im-
age for identity authentication purposes. This medical
information in binary image form is taken as watermarks.
The proposed algorithm relies on n distinct pseudo-ran-
dom (PN) matrices pairs with low correlation, where n is
the number of bits that are to be hidden. The rest of the
paper is organized as follows. Section 2 provides a brief
overview of spread-spectrum image watermarking sche-
mes in wavelet domain. Working of the proposed spread-
spectrum algorithm is explained in Section 3. Perform-
ance of the new algorithm has been analyzed in Section 4
and Section 5 provides conclusion of overall work.
2. Spread Spectrum Watermarking
in Wavelet Transform Domain
Wavelet-based watermarking has recently gained great
attention due to its ability to provide excellent multi-
resolution analysis, space-frequency localization and
superior HVS modeling [12]. DWT (Discrete Wavelet
Transform) separates an image into a lower resolution
approximation image (LL) as well as horizontal (HL),
vertical (LH) and diagonal (HH) detail components. The
process can then be repeated to computes multiple
“scale” wavelet decomposition. The dyadic frequency
decomposition of wavelet transfor m resembles the signal
processing of the HVS and thus allows adapting the dis-
tortion introduced by either quantization or watermark
embedding to the masking properties of human eye [29].
The watermarks are inserted in different decomposition
levels and subbands depending on their type, and in loca-
tions specified by a random key; thus, they can be inde-
pendently embedded and retrieved, without any interfer-
ence among them. It is evident that the energy of an im-
age is concentrated in the h igh decomposition levels cor-
responding to the perceptually significant low frequency
coefficients; the low decomposition levels accumulate a
minor energy proportion, thus being vulnerable to image
alterations. Therefore, watermarks containing crucial
medical information like doctor’s signature, patient
identification code or patient iden tification codes require
ing great robustness are embedded in higher subbands. In
general, horizontal and vertical subbands have more or
less the same characteristics and behavior, in contrast to
diagonal ones. Thereupon, watermark embedding in the
horizontal and vertical subbands guarantees increased
robustness, since their energy compaction makes them
less vulnerable to attack s.
The proposed image watermarking scheme uses
spread-spectrum technique in which, different watermark
messages are hidden in the same transform coefficients
of the cover image using uncorrelated codes, i.e. low
cross correlation value (orthogonal/near orthogonal)
among codes. A brief overview of spread-spectrum wa-
termarking technique is presented below:
2.1. Spread-Spectrum Watermarking Principle
The watermark should not be placed in insignificant re-
gions of the image or its spectrum, since many common
signal and geometric processes affect these components.
Copyright © 2011 SciRes. JIS
The problem then becomes how to insert a watermark
into the most perceptually significant regions of the spec-
trum while preserving fidelity. Clearly, any spectral co-
efficient may be altered, provided such modification is
small. However, very small changes are very susceptible
to noise. This problem can be addressed by applying
spread-spectrum watermarking which can be easily un-
derstood with spread-spectrum communications analogy
in which frequency domain of the image is viewed as a
communication channel, and correspondingly, the water-
mark is viewed as a signal that is transmitted through it
[24]. Attacks and unintentional signal distortions are
treated as noise that the immersed signal must be im-
mune to. In spread-spectrum communications, one trans-
mits a narrowband signal over a much larger bandwidth,
such that the signal energy present in any single fre-
quency is undetectable. Similarly, the watermark is
spread over many frequency bins so that the energy in
any one bin is very small and certainly undetectable.
Nevertheless, because the watermark verification process
knows the location and content of the watermark, it is
possible to concentrate these many weak signals into
single output with high signal-to-noise ratio (SNR).
However, to destroy such a watermark would require
noise of high amplitude to be added to all frequency bins.
Spreading the watermark throughout the spectrum of an
image ensures a large measure of security against unin-
tentional or intentional attack: First, the location of the
watermark is not obvious. Furthermore, frequency re-
gions should be selected in a fashion that ensures suffi-
ciently small energy in any single coefficient. A water-
mark that is well placed in the frequency domain of an
image will be practically impossible to see.
2.2. Spread Spectrum Watermark Design
There are two parts to building a strong watermark: the
watermark structure and the insertion strategy. In order
for a watermark to be robust and secure, these two com-
ponents must be designed correctly. This can be achieved
by placing the watermark explicitly in the perceptually
most significant components of the data, and that the
watermark is composed of random numbers drawn from
a Gaussian (
0,1N) distribution (where
denotes a normal distribution with mean
and vari-
ance 2
). Once the significant components are located,
Gaussian noise is injected therein. The choice of this
distribution gives resilient performance against collusion
attacks. The Gaussian watermark also gives strong per-
formance in the face of quantization [30].
- Watermark Structure: In its most basic implementa-
tion, a watermark consists of a sequence of real numbers
xx x. In practice, we create a watermark
where each value xi is chosen independently according to
- Watermarking Procedure: We extract from host digi-
tal document D, a sequence of values 12
Vvv v,
into which we insert a watermark 12
xx x to
obtain an adjusted sequence of values 12
and then insert it back into the host in place of V to ob-
tain a watermarked document D*.
- Inserting and Extracting the Watermark: When we
insert X into V to obtain W, a scaling parameter k is spe-
cified, which determines the extent to which X alters V.
Formula for computing W is
ii i
We can view k as a relative measure of embedding
strength which is also known as gain factor. A large v al-
ue of k will cause perceptual degradation in the water-
marked document.
- Choosing the Length n, of the Watermark: The
choice of length n, dictates the degree to which the wa-
termark is spread out among the relevant components of
the host digital document. In general, as the numbers of
altered components are increased the extent to which
they must be altered decreases.
- Evaluating the Similarity of Watermarks: It is highly
unlikely that the extracted mark X* will be identical to
the original watermark X. Even the act of requantizing
the watermarked document for delivery will cause X* to
deviate from X. We measure the similarity of X and X*
,* *.
sim X X
Many other measures are possible, including the stan-
dard correlation coefficient. To decide whether X and X*
match, one determines whether
im X XT, where
T is some threshold. Setting the detection threshold is a
classical decision estimati on problem [31].
3. Proposed Algorithm
This paper proposes a new DWT based spread-spectrum
watermarking algorithm using medical image cover.
Dyadic subband decomposition is performed on the ra-
diological image using Haar wavelet transform. The wa-
termark used in the algorithm is in binary image form.
Different watermark messages are hidden in the same
transform coefficients of the cover image using uncorre-
lated codes, i.e. low cross correlation value (orthogonal/
near orthogonal) among codes. For each message bit,
two different Pseudo Noise (PN) matrices namely of size
identical to the size of the wavelet coefficient matrices,
are generated. Since the security level of the watermark-
ing algorithm dep ends on the strength of its secret key, a
Copyright © 2011 SciRes. JIS
grey scale image of size 1 × 35 is used as a strong key
for generating pseudorandom sequences. Based on the
value of the bit for the message vector, the respective
two PN sequence matrices are then added to the corres-
ponding second level HL and LH coefficients matrices
respectively according to the data embedding rule as
WVkX if 0b
Where V is wavelet coefficient of th e cover image, Wis
the wavelet coefficient after watermark embedding, k is
the gain factor, X is the PN matrix and b is th e bit of wa-
termark that has to be embedded. Generation of a pair of
PN matrices for embedding each bit enhances the se-
curity of the watermarking algorithm. Following steps
are applied in data embedding process.
3.1. Data Embedding
Read the host image
MN of size
1) Read the message to be hidden and convert it into
binary sequences d
D (1
D to n)
2) Transform the host image using “Haar” Wavelet
transform and get second level subband coeffi-
cients ccA, ccH, ccV, ccD
3) Generate n different PN-sequence pairs (PN_h
and PN_v) each of size 44
using a secret
key to reset the random number generator
4) For 1
D to n, add PN sequences to ccH and
ccV components when message = 0
ccH = ccH + k*PN_h;
ccV = ccV + k*PN_v;
where k is the gain factor used to specify the strength of
the embedded data.
Apply inverse “Haar” Wavelet transform to get the fi-
nal stego (watermarked) image
3.2. Extraction of Hidden Data
To detect the watermark we generate the same pseudo-
random matrices used during insertion of watermark by
using same state key and determine its average correla-
tion with the two detail su bbands DWT coefficients. Av-
erage of n correlation coefficients corresponding to each
PN matrices is obtained for both LH and HL subbands.
Mean of the average correlation values are taken as
threshold T for message extraction. During detection, if
the average correlation exceeds T for a particular se-
quence a “0” is recovered; otherwise a “1”. The recovery
process then iterates through the entire PN sequence until
all the bits of the watermark have been recovered. For
extracting the watermark, following steps are applied to
the watermarked image:
1) Read the stego image
2) Transform the stego image using “Haar” Wavelet
transform and get ccA1,ccH1,ccV1,ccD1 coeffi-
3) Generate one’s sequences (msg) equal to message
vector (from 1 to n)
4) Generate n different PN-sequence pairs (PN_h1
and PN_v1) each of size 44
using same
secret key used in embedding to reset the random
number generator
5) For i = 1 to n
Calculate the correlations store these values in
corr_H (i) and corr_V (i).
 
correlation between
PN_h1 andccH1
corrH i
 
correlation between
PN_v1 andccV1
corrV i
6) Calculate average correlation
__2avgcorr icorrHicorrVi
7) Calculate the
mean ofallthe values
storedin _
corr n
8) Extract the hidden bit 0, using the relationship
given below
For j = 1 to n
_avgcorrjcorr n,
0msg j
9) Rearrange these extracted message
4. Performance Analysis
Performance of the proposed spread-spectrum water-
marking algorithm was tested for telemedicine applica-
tions. Experiments were carried-out using 8-bit grey
scale CT scan image of size 512 × 512 available in refe-
rence [32]. Medical information such as telemedicine
origin centre (watermark 1) and doctor’s signature (wa-
termark 2) were embedded into host CT scan image as
watermarks. These watermarks are in binary image for-
mats which add robustness by allowing recovery of the
watermarks even at low correlation between original and
extracted watermarks. Strength of watermarking is varied
by varying the gain factor in the watermarking algorithm.
Perceptual quality of th e watermarked rad iolog ical image
is measured by calculating PSNR between host and wa-
termarked image. At the receiver side, watermark is ex-
tracted from the watermarked image. Extracted water-
mark is evaluated by measuring its correlation with the
Copyright © 2011 SciRes. JIS
original watermark. Figure 1 shows the host CT scan
image and watermarked images obtained by applying
watermarking algorithm in second level LH and HL sub-
band DWT coefficients at different gain factors. Ex-
tracted watermarks along with the original watermarks
are shown in Figures 2 and 3. It is observed from Table
1 that with the increase in the gain factor, PSNR of the
watermarked image decreases and the degree of simila-
rity between original and extracted watermark increases.
To show the effect of the decomposition levels, propos ed
algorithm with gain factor 2.0 was applied for embed-
ding watermark in the horizontal and vertical subband
coefficients of level 1, 2 and 3. It is observed from Table
2 that the PSNR value of the watermarked image in-
creases and correlation between original and extracted
watermark decreases with the increase in subband level
for watermarking. Figure 4 shows the watermarks ex-
Figure 1. Original and watermarked CT scan images (a)
original image and watermarked images with gain factor;
(b) 0.5; (c) 1.5 and (d) 3.0.
Figure 2. Telemedicine centre watermarks (a) original and
extracted watermarks with gain factor; (b) 0.5; (c) 1.5; (d)
3.0 and (e) 4.0.
Figure 3. Doctor’s signature watermarks (a) original and
extracted watermarks with gain factor; (b) 0.5; (c) 1.5; (d)
3.0 and (e) 4.0.
Table 1. Effect of gain factor.
Watermark 1
(Origin centre) Watermark 2
(Doctor’s Signature )
PSNR Correlation PSNR Correlation
0.5 37.518 0.376 39.680 0.295
1.0 31.497 0.535 33.659 0.289
1.5 27.976 0.597 30.138 0.461
2.0 25.477 0.635 27.639 0.485
3.0 21.955 0.657 24.117 0.527
4 19.456 0.659 21.614 0.562
Table 2. Effect of subband levels (gain factor 2.0).
Watermark 1
(Origin centre) Watermark 2
(Doctor’s Signature )
PSNR Correlation PSNR Correlation
1 19.421 0.677 21.659 0.638
2 25.477 0.635 27.639 0.485
3 31.541 0.413 33.706 0.229
Figure 4. Extracted watermarks from (a) level
1 (b) level 2 and (c) level 3.
tracted from different levels of subband DWT coeffi-
cients. Performance of the watermarking algorithm also
depends on the size of watermark. Table 3 shows the
effect of watermark size on the performance of the pro-
posed watermarking algorithm. It is obvious that the
PSNR performance of the watermarked image decreases
with the increase in the size of the watermark, but sub-
sequently we observe an improvement in the correlation
between original and extracted watermarks. It can be also
observed from Figure 5 that larger size watermarks are
more clearly identified during extraction. To observe the
effect of host image, proposed algorithm was tested for
other medical images like MRI and ultrasound images
where watermarking is done in second level subband
coefficients considering a gain factor of 1.5 and water-
mark size of 32 × 64. Host and watermarked MRI and
ultrasound images are shown in Figure 6. It is observed
from Table 4 that the PSNR performance of all water-
marked medical images are same where as there is a little
variation in the similarity performance of original and
Copyright © 2011 SciRes. JIS
Table 3. Effect of watermark size.
Watermark 1
(Origin centre) Watermark 2
(Doctor’s Signature )
size PSNR Correlation PSNR Correlation
16 × 32 36.852 0.259 41.412 0.149
20 × 50 32.057 0.468 36.871 0.194
30 × 50 30.249 0.469 33.126 0.297
32 × 64 27.976 0.598 30.138 0.461
40 × 80 26.730 0.487 30.138 0.461
Figure 5. Extracted watermarks of different size (a) 16 × 32;
(b) 20 × 50; (c) 30 × 50; (d) 32 × 64; (e) 40 × 80.
Figure 6. Original and watermarked MRI and US im-
ages (a) original MRI image (b) original US image (c)
watermarked MRI image and (d) watermarked US im-
Table 4. Effect of host images.
(Origin centre) Watermark2
(Doctor’s Signature )
type PSNR Correlation PSNR Correlation
CT Scan 27.976 0.598 30.138 0.461
MRI 27.976 0.653 30.138 0.523
Ultrasound 27.976 0.653 30.138 0.564
extracted watermark for different medical images as
shown in Figure 7. Effect of various wavelet filters on
the proposed watermarking algorithm has also been ana-
lyzed. It can be observed from Table 5 that the Bior 6.8
wavelet filter shows slightly better performance in terms
of PSNR of the watermarked image and the correlation
of extracted watermark with the original watermark. The
scheme was also tested in terms of robustness of the im-
age watermarks to JPEG compression. Table 6 illu strates
the robustness of the watermarks, which were extracted
from a CT scan image after it was JPEG compressed by
varying the quality factor in the range of 40 to 80. Water-
marks were extracted intact after JPEG compression with
different quality factors.
5. Conclusions
This paper presented a secure spread-spectrum water-
marking scheme in wavelet transform domain. Perfor-
mance of the scheme was tested for telemedicine appli-
cations by watermarking radiological images with sensi-
Figure 7. Extracted watermarks from dif-
ferent host medical images (a) CT scan; (b)
MRI; (c) US image.
Table 5. Effect of wavelet filters.
(Origin centre) Watermark2
(Doctor’s Signature )
filter PSNR Correlation PSNR Correlation
Db1 (Haar) 27.976 0.598 30.138 0.461
Db2 27.943 0.626 30.176 0.486
Db3 27.944 0.627 30.184 0.491
Bior 6.8 28.428 0.617 30.571 0.470
Table 6. Effect of JPEG compression.
(Origin centre) Watermark2
(Doctor’s Signature )
factor PSNR Correlation PSNR Correlation
80 38.138 0.378 41.176 0.228
70 36.819 0.437 39.456 0.297
60 35.316 0.506 36.325 0.384
50 32.672 0.583 34.629 0.421
40 27.859 0.616 30.148 0.498
Copyright © 2011 SciRes. JIS
tive medical information in binary image format.
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