Digital Image Watermarking Basics

IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 8, Issue 1 (Sep. - Oct. 2013), PP 07-11
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Digital Image Watermarking Basics
Senthil Nathan.M1
, Pandiarajan.K2
, Baegan.U3
1,2 & 3
Student, III year Electronics & Communication,Karpagam College of Engineering,Coimbatore,India.
Abstract: The increasing amount of applications using digital multimedia technologies has accentuated the
need to provide copyright protection to multimedia data. This paper reviews one of the data hiding techniques -
digital image watermarking. Through this paper we will explore some basic concepts of digital image
watermarking techniques.Two different methods of digital image watermarking namely spatial domain
watermarking and transform domain watermarking are briefly discussed in this paper. Furthermore, two
different algorithms for a digital image watermarking have also been discussed. Also the comparision between
the different algorithms,tests performed for the robustness and the applications of the digital image
watermarking have also been discussed.
I . Introduction
There are three basic methods of secure communication available, namely, cryptography,
steganography and watermarking [1]. Among these three, the first one,cryptography, deals with the development
of techniques for converting information between intelligible and unintelligible forms during information
exchange that deals with the content confidentiality and access control. By using cryptography, only authorized
parties holding decryption keys can access the content (text or image). It provides the tools to secure sensitive
information. Steganography, on the other hand, is a technique for hiding and extracting information to be
conveyed using a carrier signal.
A watermarking system must allow for a useful amount of information to be embedded into the image.
Digital watermarking is the process of embedding information into digital multimedia content such that the
information (which we call the watermark) can later be extracted or detected for a variety of purposes including
copy prevention and control. Digital watermarking has become an active and important area of research,
development and commercialization of watermarking techniques is being deemed essential to help address some
of the challenges faced by the rapid proliferation of digital content.
The most important properties of any digital watermarking techniques are robustness, security,
imperceptibility, complexity, and verification. Robustness is defined as if the watermark can be detected after
media operations such as filtering, lossy compression,color correction, or geometric modifications. Security
means that the embedded watermark can’t be removed beyond reliable detection by targeted attacks.
Imperceptibility means that the watermark is not seen by the human visual system. Complexity is described as
the effort and time required for watermark embedding and retrieval. Lastly,verification is a procedure whereby
is used as a private key or public key function. Each of these properties must be taken into consideration when
applying a certain digital watermarking technique [2]. Watermarking techniques can be classified according to
the nature of data (text, image, audio or video), or according to the working (spatial or frequency) domain, also
they can be classified according to the human perception (robust or fragile) [3]. In images, the watermarking
techniques can be broadly classified into three types: (i) Visible watermark, (ii) Invisible fragile watermark and
(iii) Invisible robust watermark [2], [3], which has wider currency and use. However all these mentioned classes
can be applied by using software, hardware or both together. Fig. 1 illustrates a generic watermarking
scheme.there are two watermarking techniques using software namely spatial domain and transform domain.
Fig .1. Generic watermarking scheme [14]

II . Spatial Domain
An analogue image can be described as a continuous function over a two-dimensional surface. The
value of this function at a specific coordinate on the lattice specifies the luminance or brightness of the image at
that location. A digital image version of this analogue image contains the sampled values of the function at
discrete locations or pixels. These values are said to be the representation of the image in the spatial domain or
often referred to as the pixel domain. Spatial embedding inserts message into image pixels. The oldest and the
most common used method in this category is the insertion of the watermark into the least significant bits (LSB)
of pixel data [4][5][6].
In [7] they have implemented a simplest model of watermark technique. It has the ability to insert an
invisible watermark into a spatial domain of a base-image. This technique yields marked-images with high
imperceptibility and robustness quality. The algorithm provides high level of security by generating encryption
key which is used to extract the watermark later; also, the algorithm is able to randomize the location of the
watermark in different base-images.
III . Transform Domain
Transform domain embeds a message by modifying the transform coefficients of the cover message as
opposed to the pixel values. Ideally, transform domain has the effect in the spatial domain of apportioning the
hidden information through different order bits in a manner that is robust. There are a number of transforms that
can be applied to digital images, but there are notably three most commonly used in image watermarking. They
are Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform
(DWT).
A . DISCRETE FOURIER TRANSFORM
Fourier Transform (FT) is an operation that transforms a continuous function into its frequency
components. The equivalent transform for discrete valued function requires the Discrete Fourier Transform
(DFT). In digital image processing, the even functions that are not periodic can be expressed as the integral of
sine and/or cosine multiplied by a weighing function. This weighing function makes up the coefficients of the
Fourier Transform of the signal. Fourier Transform allows analysis and processing of the signal in its frequency
domain by means of analyzing and modifying these coefficients.
Pereira proposed a method for copyright protection by embedding a digital watermark in the DFT domain
[8]. The properties of this technique based on polar maps for the accurate and efficient recovery of the template
in an image which has undergone a general affine transform. In this technique, the watermark is composed of 2
parts: one is a template which contains no information in itself but can detect any transformations undergone by
the image, and another one is a spread spectrum message that contains the hidden information. The length of the
hidden information is supposed to be short and it is subjected to a preprocessing algorithm to produce the new
message of length. Prior to embedding the hidden message, the luminance component of the cover image is
extracted and is used to calculate the DFT coefficients. The hidden data and the template are then embedded in
these coefficients. The template is embedded along 2 lines in the cover image which go through the origin and
its purpose is to detect any attacks (transformation) the image has undergone
B . DISCRETE COSINE TRANSFORM
Discrete Cosine Transformation (DCT) transforms a signal from the spatial into the frequency domain
by using the cosine waveform. DCT divide the information energy in the bands with low frequency and DCT
popularity in data compression techniques such as JPEG and MPEG.The DCT allows an image to be broken up
into different frequency bands, making it much easier to embed watermarking information into the middle
frequency bands of the image. Here the middle frequency bands chosen such that they minimize to avoid the
visual important parts of the image (low frequencies) without over-exposing themselves to removal through
compression and noise attacks (high frequencies). FL is use to denote the lowest frequency components of the
block, while FH is used to denote the higher frequency components. FM is Chosen as the embedding region as
to provide additional resistance to lossy compression techniques [9].
Chandra have proposed a robust watermarking method using MDKP [10].it has greatest robustness
against various attacks and preserves the image quality after water marking. It embeds a watermark into an
image in DCT domain. The image is divided into blocks and each block is processed using Multi Dimensional
Knapsack Problem (MDKP) and in turn converts to spatial domain. The watermark is extracted from the image
and compared with the original image. The measurement of quality of image is also concerned. this scheme
exhibits better performance in robust digital watermarking.

C . DISCRETE WAVELET TRANSFORM
Wavelet transform decomposes an image into a set of band limited components which can be
reassembled to reconstruct the original image without error. Since the bandwidth of the resulting coefficient sets
is smaller than that of the original image, the coefficient sets can be down sampled without loss of information.
Reconstruction of the original signal is accomplished by up sampling, filtering and summing the individual sub
bands. The basic idea of discrete wavelet transform(DWT) in image process is to multi-differentiated
decompose the image into sub-image of different spatial domain and independent frequency district [11][12].
Then transform the coefficient of sub-image. After the original image has been DWT transformed, it is
decomposed into 4 frequency districts which is one low-frequency district(LL) and three high-frequency
districts(LH,HL,HH). If the information of low-frequency district is DWT transformed, the sub-level frequency
district information will be obtained. A two-dismensional image after three-times DWT decomposed can be
shown as Fig.2. Where, L represents low-pass filter, H represents high-pass filter. An original image can be
decomposed of frequency districts of HL1, LH1, HH1. The low-frequency district information also can be
decomposed into sub-level frequency district information of LL2, HL2, LH2 and HH2. By doing this the
original image can be decomposed for n level wavelet transformation. The informaton of low frequency district
is a image close to the original image. Most signal information of original image is in this frequency district.
The frequency districts of LH, HL and HH respectively represents the level detail, the upright detail and the
diagonal detail of the original image.
In [13] Shivani Garg proposed a new model for the DWT method of watermarking. The Proposed
method decomposes cover image (original image), which is to be watermarked using DWT. The watermark is
embedded to the specified DWT coefficient of the cover image. A new DWT based spread spectrum
watermarking technique is proposed on the basis of embedding of various sequences in the DWT coefficients.
The algorithm is Column wise DWT Coefficients Embedding Algorithm (CCE). In this method columns of
DWT coefficients are taken for watermarking. The proposed algorithm is experimented on different sequences.
Performance of the algorithm is analyzed by varying the gain factor and by inserting different sequences.
Simulation results show that the proposed method achieves higher security and robustness especially in the case
of Kasami sequence.
Fig . 2 sketch map of image DWT decomposed
IV . Watermarking Algorithms
A . SPATIAL DOMAIN ALGORITHM
Embedding algorithm [14]
1) Reorganize size of the base-image to be [M × M].
2) Reset the contents of the LSB plane in the baseimage.
3) Reorganize color and size of the watermark to be [N × N] gray image.
4) Extract the all bit plane details from the watermark.
5) Shift the extracted values to right.
6) Add the shifted bit plane of the watermark into the LSB plane of the base-image.
7) Repeat steps (4th, 5th and 6th) m-times.
8) Finally, result is a watermarked-image.
Note that: watermarks’ size should not exceed baseimages’
size, e.g. ([N × N] = [M × M] / m).
Extraction algorithm
1) Extract the LSB bit plane from the marked image.
2) Shift the extracted contents by left.
3) Repeat 1st and 2nd steps, (m) times.

4) Result is watermark retrieved.
B . FREQUENCY DOMAIN ALGORITHM
Embedding algorithm [14]
1) Reorganize color and size of the base-image to be [M ×M] gray color.
2) Compute 2D wavelet transform for the base-image.
3) Initiate the weight of the watermarking.
4) Reorganize size and color of the watermark to be [M ×M] gray image.
5) Divide the transformed base-image into 4-blocks, namely, LL, LH, HL and HH respectively.
6) Multiply watermark by watermarking weight and then add the result to the blocks of the base-image.
7) The inverse wavelet transform is then taken to get the watermarked-image.
Extraction algorithm
On the other hand the extraction algorithm can be done by taking the forward wavelet transform of the
watermarked image and then subtracted it from the base-image to get the watermark.
V. Watermarking Robustness Test
Many different methods can be used to test whether a watermark can survive different changes to the
image it is
embedded in. Here is a browsing of the popular of these methods [3]:
Horizontal Flipping: Many images can be flipped horizontally without losing quality. Few
watermarks survive flipping, although resilience to flipping is easy to implement.
Rotation & Cropping: A small rotation with cropping doesn’t reduce image quality, but can make
watermarks undetectable as rotation realigns horizontal features of an image used to check for the presence of a
watermark.
JPEG Compression/Re-compression: JPEG is a widely used compression algorithms for images and
any watermarking system should be resilient to some degree to compression or change of compression level.
Scaling: Uniform scaling can increase/decrease an image by the same (%) rate in the horizontal and
vertical directions. Non-uniform scaling can increase/decrease the image horizontally and vertically at different
(%) rates. Digital watermarking methods can be resilient only to uniform scaling.
5) Dithering: It approximates colors by alternating two available similar colors from pixel to pixel. If
done correctly this method can completely obliterate a watermark, however it can make an image appear to be
“patchy” when the image is over-dithered.
6) Mosaic: A mosaic attack doesn’t damage the watermarked-image or make it lose quality in any way,
but still enables the image to be viewed in. To the viewer a “mosaic” image appears to look the same as the
original. This means that the watermark cannot be detected, as a problem common to all image watermarking
schemes is that they have trouble embedding watermarks into small images, (less than 256 pixels in height or
width).
VI . Comparision
Advantages of DWT over DCT:
Wavelet transform understands the HVS more closely than the DCT.
Wavelet coded image is a multi-resolution description of image. Hence an image can be shown at
different levels of resolution and can be sequentially processed from low resolution to high resolution [15].
Disadvantages of DWT over DCT:
Computational complexity of DWT is more compared to DCT'. As Feig (1990) pointed out it only takes
54 multiplications to compute DCT for a block of 8x8, unlike wavelet calculation depends upon the length of
the filter used, which is at least 1 multiplication per coefficient.
Advantages of DFT over DWT and DCT:
DFT is rotation,scaling and translation (RST) invariant. Hence it can be used to recover from geometric
distortions, whereas the spatial domain, DCT and the DWT are not RST invariant and hence it is difficult to
overcome from geometric distortions [15].
VII . Digital Watermarking Applications
Digital image watermarking have a wide range of applications in the field of digital multimedia.The
following are some of the applications of digital image watermarking [16].

A . Copyright protection: The most important application of watermarking is to provide copyright protection.
Copyright protection requires high level of robustness so that the embedded watermark can not be removed
without data distortion This watermark is extracted to show as proof if someone claims the ownership of the
data.
B . Finger Printing: A robust watermarking algorithm is required for this application. Watermark is embedded
in digital data to trace the source of illegal copies. Information related to customer like serial number or
customer identity information is used as watermark.
C . Content Authentication (integrity protection): The objective of this application is to detect modification
in data. To verify the authenticity of the received data watermark is embedded in host data. A fragile
watermarking algorithm is required in this case.
D . Broadcast Monitoring:The main use of broadcast monitoring is to protect TV products like news items
from illegal transmission . Watermark is embedded in commercial advertisements. Automated monitoring
system can verify
whether the advertisements are broadcasted as contracted or not.
E . Indexing: Search engine use this technique to retrieve the required data in a short period of time and without
any ambiguity. In indexing, Comments and markers or key information related to the data is inserted as
watermark.
VIII . Conclusion
This paper presents an overview of digital image watermarking. First we introduced the various
watermarking techniques and classified them into three types: visible watermark, invisible fragile watermark
and invisible robust watermark. Spatial domain and transform domain watermarking are briefly discussed
here.Two different algorithms for the digital image waterwarking are also been discussed.Comparisions between
the various watermarking technologies are also been reviewed.Thus the digital image watermarking proves to be
a promising technology for the pupose of data hiding.
REFERENCES
[1]. N. S. Kulkarni, I. Gupta, and S. N. Kulkarni “A Robust Image Encryption Technique based on Random Vector” , IEEE Computer
Society. IEEE conference publications,in international conference on Emerging trends in engineering and technology (ICETET),pp.
15-19,2008.
[2]. P.B.Khatkale, D.G.Lokhande, Srescoe, “Digital Watermarking Algorithm for Color Images”, IOSR Journal of Engineering
(IOSRJEN) ,Vol. 3, Issue 3,pp. 01-09, Mar 2013.
[3]. S. Jayaraman, S. Esakkirajan, and T. Veerakumar. “Digital Image Processing”, McGraw-Hill, 2009.
[4]. Cox.I, Miller.M, Bloom.J, Fridrich.J,Kalker. T “Digital Watermarking and Steganography”, 2nd
Edition. ISBN: 978-0123725851
[5]. Wang, R. Z., Lin, C. F., Lin, J. C. “Image hiding by optimal LSB substitution and genetic algorithm ”, Pattern Recognition, vol. 34,
pp.671- 683, 2001.
[6]. Swanson.M.D,Kobayashi. M, Tewfik.A.H. “Multimedia Data-Embedding and watermarking Technologies”,in Proceeding of
IEEE, vol. 86,no.6, pp.1064 – 1087, 1998.
[7]. Mustafa Osman Ali, Elamir Abu Abaida Ali Osman, Rameshwar Row, “Invisible Digital Image Watermarking in Spatial Domain
with Random Localization”, International Journal of Engineering and Innovative Technology (IJEIT),Vol.2, Issue 5, pp.237-
231,Nov 2012.
[8] . Pereira.S , Pun.T, “Robust Template Matching for Affine Resistant Image Watermarks ”, IEEE Transactions on Image Processing,
vol.9,issue.6,pp.1123-1129, 2000.
[9]. Keta Raval, Rajni Bhoomarker, Sameena Zafar “Implementation of Digital Watermarking For Image Security with EBCOT
Algorithm and Error Correcting Codes”,International Journal of Engineering and Advanced Technology (IJEAT), Vol.2, Issue.3,
pp.281-285,Feb 2013.
[10] . A. Chandra,T. Kavitha , “Robust digital watermarking using MDKP”, International Journal of Scientific and Research Publications,
Vol.2, Issue 6, pp.1-4,Jun 2012.
[11]. Ghouti.L, Bouridane.A , Ibrahim MK, “Digital image watermarking using balanced multiwavelets” , IEEE Transactions on Signal
Processing, vol.54,issue.4, pp. 1519-1536,2006.
[12]. Reddy A.A, Chatterji B.N, “A new wavelet based logo watermarking scheme” , Conference Pattern Recognition
letters,vol.26,issue.7, pp. 1019-1027, 2005.
[13]. Shivani Garg, Ranjit Singh, “An Efficient Method for Digital Image Watermarking Based on PN Sequences”, International Journal
on Computer Science and Engineering, Vol. 4 No. 09,pp.1550-1561, Sep 2012.
[14]. Mustafa Osman Ali , Rameshwar Rao “Digital Image Watermarking Basics, and Hardware Implementation”, International Journal
of Modeling and Optimization, Vol. 2, No. 1,pp. 19-24, Feb 2012.
[15]. Prabhishek Singh, R S Chadha “Review to Digital Watermarking and a Novel Approach to Position the Watermark in the Digital
Image”,International Journal of Engineering and Advanced Technology (IJEAT), Vol.2, Issue.4,pp.24-27, Apr 2013.
[16] . Er. Jaspreet Kaur , Er. Karmjeet Kaur, “Digital Watermark: A Study” , International Journal of Advanced Research in Computer
Science and Software Engineering,vol. 2,issue.8, pp. 159-163,Aug 2012.

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