Multi-Layer Multiple-Key Digital Image Watermarking Scheme
Lim Say Yarn, Khoo Bee Ee
School of Electrical & Electronic Engineering
University Sains Malaysia, Engineering Campus,
14300 Nibong Tebal, Seberang Perai Selatan, Penang
Tel: +604-5937788, Fax: +604-5941023
{eeyarn@eng.usm.my, beekhoo@eng.usm.my}
Abstract
As the result of the rapid usage of internet, the issue of
Intelligent Property Right (IPR) becomes more and more
important. The widespread distribution of digital media and
imagery over internet makes the protection of copyright
become increasingly significant. Watermarking technique was
introduced to protect the owner’s copyright on their media.
This paper introduces a new watermarking scheme which
embeds the watermark into the digital images for the protection
of owner’s copyright. The proposed watermarking scheme
embeds multiple watermarks into the digital image and
therefore generates multiple key to extract the watermarks. The
image is first decomposed into frequency domain using
stationary wavelet transform and the watermark is embedded
into selected location of frequency coefficients using
significant key. The watermark embedding algorithm takes into
account the contrast of the frequency coefficients when
embedding the watermark information in order to reduce the
effects of the modifications that can be perceived by the human
eyes. This new watermarking scheme shows its robustness
against some common signal processing attacks.
Keywords---Digital image watermarking, Copyright protection.
1. Introduction
Nowadays, most of the data are saved in digital format.
The advantages of saving the data in digital format are easy for
creation, modification and distribution. The rise of Internet
usage over the past few years has produced a diverse range of
dynamic and highly interactive environment for this digital
information exchange. However, the efficiency of information
exchange and manipulation results the issue of copyright
protection become increasingly significant. The possibility of
unlimited copying of digital media without any loss of quality
may cause the media producers and content providers a
considerable financial loss. Watermarking technique was
introduced to protect the owner’s copyright on their media
[Hartung and Kutter. 1999].
Digital watermarking is a technique that hides the owner’s
copyright information, also called watermark, imperceptibly
into the digital media before distributed it in the internet [Cox
et al. 2002]. This copyright information can be extracted out for
verification when it is required. The performance of
watermarking schemes can be evaluated based on a set of
defining properties [Cox et al. 2002]. For copyright protection,
these properties include:

Perceptual invisibility, where the watermarks are
imperceptible after embedded into the image.

Robustness to common signal processing operation,
such as lossy compression, halftoning, spatial
filtering, printing and scanning, and geometric
distortion.

Accurate detection of a correct watermark from a
watermarked image. It also refers as false positive
rate.

Ability to determine the true owner of the image
based on the security key and the extracted
watermark information.
Early work on digital watermarking for still image focused
on information hiding on spatial domain. For example, ChangHsing Lee, and Yeuan-Kuen Lee in [Chang-Hsing and YeuanKuen. 1999] embed the watermark by modifying the image
intensity value. In [Van Schyndel et al. 1994], R. G. van
Schyndel et al. proposed a watermarking scheme which embed
the watermark by changing the least significant bit (LSB) of the
image intensity value. However, watermark that is embedded in
spatial domain are claimed that not robust enough for the use of
copyright protection [Wolfgang et al. 1999], [Cox et al. 1997].
The watermarks that are embedded in spatial domain will easily
be removed by common filtering process. Besides that, some of
the spatial domain watermarking scheme are unable to detect
the embedded watermark when the watermarked image
underwent geometrical distortion like additional of noise.
Therefore, recent watermarking research focused on embedding
watermark in frequency domain. Cox et al. in [Cox et al. 1997]
proposed a well-known frequency domain watermarking
scheme called spread spectrum watermarking technique. He
firstly decomposed the host image into frequency domain using
Discrete Cosine Transform (DCT). His watermark was a
construction of independent and identically Gaussian random
vector watermark. The watermark is embedded in a spreadspectrum-like fashion of the perceptually most significant
spectral component of the DCT coefficients of host image. The
watermarked image was more robust against common signal
processing operations as compared with spatial domain
watermarking scheme because the watermark spread all over
the frequency coefficients. Xia, Boncelet, and Arce [Xia et al.
1997] used the Discrete Wavelet Transform (DWT) to
decompose the host image and embedded the modeled
Gaussian noise watermark into the middle and high frequency
bands of DWT coefficients. The decoding process calculated
the cross correlation between the DWT coefficients of host
image and watermarked image. Muhammad Shafique Shaikh
and Yasuhiko Dote in [Muhammad Shafique Shaikh and
Yasuhiko Dote. 2003] proposed a similar DWT watermarking
scheme. They transform the watermark and host image into
frequency domain and embed the watermark coefficients at
different transformation level with a novel procedure.
Watermark is extracted by inverse transformation at every level
and by feeding the residual marked image to the subsequent
level. Extracted watermark is then estimated by taking mean
value of the watermarks obtained from every level. The
watermarking scheme proposed by Kunder and Hatzinakos
[Kundur and Hatzinakos. 1997] also embedded the watermark
in the wavelet domain. The strength of the embedded
watermark was based on the contrast sensitivity value of the
host image. The result was robust to additive noise, rescanning
and JPEG compression.
In this paper, we proposed a new wavelet transformation
watermarking scheme for digital images. This watermarking
scheme was motivated from the watermarking scheme
proposed by Chang-Hsing Lee, and Yeuan-Kun Lee in [ChangHsing and Yeuan-Kuen. 1999]. Unlike their watermarking
scheme, our watermark was embedded in frequency domain.
Most of the network-based images are stored in compressed
format and that wavelets play an important role in the
upcoming compression standards such as JPEG2000. Therefore,
in our watermarking scheme, we implement wavelet transform,
which is using stationary wavelet transform (SWT) [Nason and
Silverman. 1995] on the host image. We transform the host
image into SWT coefficients before the watermark is
embedded. Embedding the watermark in high-pass wavelet
coefficients will not impact the watermarked image visual
fidelity [Cox et al. 1997].
Besides that, to solve the draw back of symmetric
watermarking scheme which is mentioned in [Scott Craver and
Katzenbeisser. 2001], there are more than one watermark will
be embedded into the host image in our watermarking scheme.
Embedding multiple watermarks into the host images generates
multiple security keys for the watermark extraction process. An
attacker may remove one or two of the embedded watermark if
he knows the watermark extraction key. However, the attacker
may not be able to remove all the embedded watermarks if he
lacks of all the watermark extraction keys. Closet point attack
may remove all the embedded watermarks [Mauron Barni et al.
2003]. Unfortunately, it is hard to remove the watermarks
without degrading of the perceptual quality of the image.
Therefore, in our watermark extraction process, the correct
security key is required to extract a particular watermark. In
real application process, client will receive only one
watermarking key for the verification process so that the
disclosure of that watermarking key will not remove all the
embedded watermarks. Thus, the image’s copyright is still
under protection.
In the following section, we describe the proposed
watermarking scheme in detail. We also test the watermarking
scheme against some common signal processing distortion. The
experimental result shows that the watermark still can be
extracted and correctly identified after the attacks.
The embedded watermark is a visually recognizable
binary logo rather than a randomly generated sequence bits.
Thus, in watermark extraction process, the extracted watermark
can easily be identified by human eyes. The watermark
embedding algorithm adaptively modifies some selected SWT
coefficients of host image and these modifications are not
noticeable to human eyes.
In the following subsection, we describe the watermark
permutation process, watermark embedding and extraction
process. General block diagram of the proposed watermarking
scheme is shown in Figure 1.
2.1. Watermark Permutation Process
The original watermark is a binary logo. The watermark
logo is first permuted into scrambled data before insertion
process. This watermark permutation process prevents the
watermark from tampering or unauthorized access by attackers.
The watermark permutation process is same as that proposed in
[Hsu and Wu. 1999]. Initial random seed of this permutation
process was saved as a part of the security key in watermark
extraction process.
2.2. Watermark Embedding Process
The host image is first decomposed into frequency domain
using stationary wavelet transform (SWT). The advantage of
using SWT is the size of the decomposed coefficients is
equivalent to the size of the host image. Thus, the number of
coefficients that can embed one bit watermark is increased. To
keep the invisibility of watermark on watermarked image, the
watermark is embedded into the middle and high frequency
band of SWT coefficients. A bit of watermark pixel value (0 or
1) is embedded in a block of SWT coefficients. SWT
coefficients in the block are adaptively modified to maximize
the robustness. The watermark insertion method is described as
follows:
1. Decompose host image using Stationary Wavelet
Transform (SWT).
2. Select a block, P of size nxn SWT coefficients. It is
done by generate a pseudo-random number sequence
using a seed value k.
3.
Compute the average coefficient
p mean , maximum
p max , minimal coefficient p min and
standard deviation  p of the block P .
Classify every coefficient in block P into 2
categories, Z H and Z L , using p mean :
cij  Z H if cij  pmean
coefficient
4.
cij  Z L
where
c ij
block
P.
if cij  pmean ,
represents the SWT coefficient of the
mH and m L of
2. The Proposed Watermarking Scheme
5.
Compute the mean values,
categories.
The proposed wavelet-based watermark embedding and
extraction scheme is similar to the watermarking scheme which
is proposed in [Chang-Hsing and Yeuan-Kuen. 1999].
However, instead of embedding the watermark into the spatial
domain, we embedded the watermark into the stationary
wavelet transform coefficients of host image.
6.
Given the value of watermark
the SWT coefficients in
rules:
these 2
bw is 0 or 1, modify
block P according to the
if bw  1;
T
cij  p max
if cij  m H ,
cij  p mean
if m L  cij  p mean ,
'
'
cij  cij   p otherwise,
'
where
if bw  0;
if cij  m L ,
cij  p mean
if p mean  cij  m H ,
'
cij  cij -  p otherwise,
'
where
7.
8.
9.
cij
'
is the modified SWT coefficient.
The modified block of SWT coefficients,
Pnew is
then positioned back to the same location as from the
host image.
Repeat the process to embed another watermark by
changing different seed value k. This seed value k is
saved as a part of the security key besides the key
that used in watermark permutation process.
Reconstruct the new set of SWT coefficients to get
the watermarked image.
From the embedding algorithm, the watermark bit is
embedded depending on the content of each block of SWT
coefficients. The larger the contrast value (
p max , and p min ),
the more the SWT coefficients is modified. Besides that, the
sum of SWT coefficients of original block P will be larger
than that of modified block
value
bw is
Pnew
mn
m


 A Bmn  B
if the inserted watermark

n



2 
2

  Amn  A   Bmn  B 
 m n
 m n

Amn and A represent the extracted watermark value and
its average value respectively, and
cij  p min
'
 A
Bmn and B represent
the
original watermark value and its average value respectively.
We calculate the two dimensional correlation coefficient
between the extracted logo and the original logo and compare it
with a threshold value to decide if the watermarks match. The
threshold value was chosen as T = 0.50 based on the fine
tuning experimental result.
3. Performance Evaluation
In our experiment, we have chosen a three-level stationary
wavelet transform using Haar filter for a size 512x512 Lena
image and our watermark logo size is 55x55. The block P was
chosen as size 4x4. Therefore, the minimum number of
watermark that can be embedded was four. In this paper, we
just embed three same watermark logos into the selected
location of the host image. Figure 2 shows the output images of
the proposed watermarking scheme and the differences
between host image and watermarked image. Watermarked
image retained the quality of the host image while keeping the
embedded watermark imperceptible. Table 1 shows the
correlation coefficient measurement for the watermark logo
extraction process. As shown in Table 1, the correlation
coefficient value for the extracted watermarks from a
watermarked image without any attacks are all above the
threshold value.
1. On the other hand, if the inserted watermark
value is 0, the sum of coefficients of block
Pnew
will be
smaller than that of P .
2.3. Watermark Extraction Process
The watermark extraction process is the reverse order of
the embedding process. The original image is required for the
extraction process. Based on the security key, the block that
embedded watermark was extracted. The sum of the SWT
coefficients of host image and watermarked image,
Sw
So
and
respectively, in the block are computed. The retrieved
watermark bit value is determined by comparison of the two
resultant values:
bw  1
if S w  S o ,
bw  0 otherwise.
The decoded watermark bit values,
bw
'
s, are then inversely
permuted to get the reconstructed watermark. The decision as
whether there is a watermark in the image is based on the
correlation coefficient measurement, using the formula:
Figure 2 Watermarking Scheme Output Images
Performance of the proposed approach is evaluated under
‘Salt & Pepper’ noise attack and JPEG compression. Figure 3
shows the attacked watermarked images while Figure 4 shows
one of the extracted watermark logo from the JPEG
compression 90%. The watermarked image was undergoes
‘Salt & Pepper’ noise attack with noise density (ND) 0.005 and
0.01. The extracted watermark logo’s correlation coefficient
values are shown in Table 1. From the result, the correlation
coefficient values are above the threshold value except two
watermark logo for noise density 0.01 of ‘Salt & Pepper’ noise
attack. Therefore, we may conclude that our watermarked
images are robust to a level of ‘Salt & Pepper’ noise attack.
References
Hartung, F. and Kutter, M. 1999. Multimedia Watermarking
Techniques. Proc. IEE, vol. 87, IEEE Press, Piscataway,
N.J., pp. 1079-1107, July.
Cox, I. J., Matthew L. Miller, Jeffrey A. Bloom. 2002. Digital
Watermarking. Morgan Kaufmann Publishers.
Figure 3 Watermarked Images with Noise Attack
and JPEG Compression
Chang-Hsing, Lee and Yeuan-Kuen, Lee. 1999. An Adaptive
Digital Image Watermarking Technique for Copyright
Protection. IEEE Transactions on Consumer Electronics
Vol. 45, No. 4, November.
Van Schyndel, R. G., Tirkel, A. Z., and Osborne, C. F. 1994. A
Digital Watermark. In Proc. Int. Conf. Image Processing
(ICIP), vol. 2, pp. 86-89.
Wolfgang, R. B., Podilchuk, C. I. and Delp, E. J. 1999.
Perceptual Watermarks for Digital Images and Video.
Proceedings of the IEEE, vol. 87, no. 7, pp. 1108-1126.
July.
Figure 4 Extracted Watermark Logo for JPEG
Compression 90%
Lena image also compressed with quality 95%, 90% and
85% after the watermark logo is embedded. From Table 1, all
the correlation coefficient values are above the threshold value,
except the correlation coefficient value for Logo 1. Although
one of the embedded watermark logo cannot be detected, the
others two watermark logos can successfully be detected. Thurs,
we may conclude that the watermark is positively detected after
the watermarked image has been undergone JPEG compression.
Images
Watermarked
Image
‘S&P’ Noise
( ND=0.005)
‘S&P’ Noise
( ND=0.01)
JPEG 85%
JPEG 90%
JPEG 95%
Correlation Coefficient Value (T=0.45)
Logo 1
Logo 2
Logo 3
0.6975
0.6114
0.6585
0.6544
0.5352
0.5486
0.6245
0.4430
0.4709
0.3158
0.3856
0.4977
0.5164
0.5541
0.5933
0.5000
0.5541
0.6304
Table 1 Correlation Coefficient Measurement
4. Conclusions
We described a stationary wavelet transform
watermarking scheme where the embedding algorithm can
embed multiple watermarks into one image. To extract each of
the watermarks, the correct security key is required. Therefore,
our watermarking scheme offers extra security feature compare
to others well-known watermarking schemes. Besides that, the
embedded watermark was imperceptible under human visual
inspection and the produced watermarked image closely
retained the quality of the original image. Robustness of the
proposed algorithm is evaluated and the embedded watermark
is successfully detected.
Cox, I. J., Joe Kilian, Tom Leighton, and Talal Shamoon. 1997.
Secure Spread Spectrum Watermarking for Multimedia. In
IEEE Transaction on Image Processing, 6, 12, 1673-1687.
Xia, X., Boncelet, C., and Arce, G. 1997. A Multiresolution
Watermark for Digital Images. Proc. IEEE Int. Conf. on
Image Processing, vol. I, pp. 548-551. Oct.
Muhammad Shafique Shaikh and Yasuhiko Dote. 2003. A
Watermarking Scheme for Digital Images Using Multilevel
Wavelet Decomposition. Malaysian Journal of Computer
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Nason, G. P. and Silverman, B. W. 1995. The stationary
wavelet transform and some statistical applications. Lecture
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Scott Craver and Katzenbeisser. 2001. Security Analysis of
Public-key Watermarking Schemes. Proceedings of the
SPIE vol. 4475, Mathematics of Data/Image Coding,
Compression, and Encryption IV, pp. 172-182, July.
Hsu, C.-T., and Wu, J.-L. 1999. Hidden Digital Watermarks In
Images. IEEE Transaction on Image Processing. Vol. 8, No
1, pp. 58-68.
Mauron Barni, Franco Bartolini, and Teddy Furon. 2003. A
General Framework for Robust Watermarking Security.
Signal Processing, vol. 83, Issue 10, pp. 2069-2084, Oct.
Kundur, D. and Hatzinakos, D. 1997. A Robust Digital Image
Watermarking Method Using Wavelet-Based Fusion. ICIP,
Oct. Vol. I, pp. 544-547.
Embedding
Attacks
Security Key
Extraction
Security Key
Original
Image
Watermarked
Image
SWT
Watermark
Insertion
ISWT
Original
Image
SWT
Scrambling
Process
Watermark
Extraction
SWT
De-scrambling
Process
Correlation
Measurement
Figure 1 Block Diagram of Proposed Watermarking Scheme