International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue7- July 2013
A Review of hybrid digital watermarking
Kusum Yadav #1, Akhil Kaushik*2
1
PG Student, CSE Department, M.D.U Rohtak, Haryana, India
2
Assistant Professor, M.D.U Rohtak, Haryana, India
Abstract—With the advent of Internet, digital data has become so
redundant and easily accessible across the globe, that numerous
unauthorized users are taking its advantage and ripping the
actual authors of the ownership over their digital content. Digital
watermarking provides a way to protect the multimedia data,
which is available through online resources. It facilitate in
solving the problems associated with copy right protection, copy
protection etc. The watermark should be robust in such a way
that it is recoverable, even if a part of it is altered. There are
different methods for embedding and extracting the watermark
from the original image. In this paper three different methods of
embedding and extracting the watermark have been discussed.
The First method focuses on implementing watermarking
technique through DWT(Discrete wavelet transformation) and
SVD(Singular value displacement. Another methodology
conferred uses the concept of superimposition using a secret key.
The final scheme uses the concept of artificial neural network for
digital watermarking. The comparison of above mentioned three
techniques is done by considering effects of various types of
attacks like cropping, rotation, filtering etc. against robustness
of the watermarking approaches.
Index Terms—Digital Watermarking, DWT, DCT, SVD.
different frequencies by suitable transform. The transformed domain
coefficients are then modified by the watermark. Recovery process of
watermark is simply achieved by applying inverse of transformation.
Transformation or frequency domain is categorized in the following:
1) Discrete Cosine Transformation (DCT).
2) Discrete Wavelet Transformation (DWT).
3) Discrete Fourier Transformation (DFT).
In the Discrete Cosine Transformation process the original image
is first divided into 8x8 blocks of pixels. The 2-dimensional DCT is
performed on each block to obtain the DCT coefficients for each 8x8
pixel block. This results in 64 DCT coefficients for each block.
Coefficients of middle frequency range are picked from the DCT
coefficients and modified to their relative values encoded as one or
zero in order to embed the watermark bits. Watermark image is then
obtained by performing inverse of DCT of each block.
The Discrete Wavelet Transformation consists of multi-scale
frequency decomposition of an image. Image is firstly decomposed
into four parts of high, middle and low frequencies (i.e. LL, HL, LH
& HH) by sub sampling horizontal and vertical channels using subband filters. These sub-bands can be decomposed further to acquire
the next coarser scaled wavelet coefficients.
The Discrete Fourier Transformation is useful to select the
adequate parts of the image for embedding the watermark in order to
obtain the best compromise between visibility and robustness.
Introduction
D
igital watermarking is the process of protecting the multimedia
data from unauthorized attacks like cropping, rotation, filtering,
scaling etc. The watermarking can be classified into following
types:
1) Text watermarking.
2) Audio watermarking.
3) Video watermarking.
4) Image watermarking.
Due to growth of internet and World Wide Web, there are various
problems associated with the image watermarking and various
methods are designed and implemented to protect the images from
unauthorized attacks. According to the domain in which watermark is
to be embedded these techniques are divided into two broad
categories:
1) Spatial domain
2) Transformation domain
Watermarking algorithm that relies on spatial domain hides the
watermark by modifying the pixel values of the host image.
Transformation domain is also called as Frequency domain and in
this technique the host image is first converted into
ISSN: 2231-5381
Fig. 1. Digital Watermarking Process
These techniques may used in combination with each other and
they can also be used in amalgamation with other methodologies like
Singular Value Displacement (SVD). Apart from the above
mentioned modus operandi, there is an additional technique that is
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue7- July 2013
gaining popularity and that method is known as superimposition
technique.
Basically the digital watermark algorithm consists of four
important parts:
1) Watermark.
2) Encoder.
3) Decoder.
4) Original image.
Encoder helps in embedding the watermark into the original image
to protect the original image from various types of attacks. Decoder
helps in extracting the watermark from the original image. The block
diagram for embedding and extracting the watermark is shown in
figure1. Besides using these encoder and decoder for embedding the
watermark into the original image, the original image will be
vulnerable to various kinds of attacks like scanning. For providing
more security, authentication mechanisms may be used.
Based on the presence or absence of the original content, the
watermarking scheme can be classified as:
1) Blind scheme: It does not require the presence of the original
content at the time of watermark detection.
2) Non-blind scheme: It requires the presence of original content at
the time of watermark detection
Earlier non-blind schemes were popular, but as more research has
taken place in the field of digital watermarking blind schemes have
also caught the attention. In this paper three research papers are
explored to study three several watermarking techniques.
2)
3)
4)
5)
6)
W= Uw * Sw * Vw T
(1)
Apply Haar wavelet and decompose the image into four subbands LL, LH, HL, and HH.
Apply SVD to the cover image
H= UH * SH * VH T
(2)
Replace the singular values of the HH band with the singular
values of the watermark.
Apply inverse of SVD to obtain the modified HH band.
H’= UH * SW * VH T
(3)
Apply inverse of DWT to obtain the watermarked cover image.
Fig. 2. Embedding Watermark in Lena Image
Authors
Table 1: List of research papers discussed
Corresponding
research paper
Akshay Kumar Gupta &
Mehul Raval
Kritika Singla & Sumit
Kaur
ME1 Shi-chun, LI Renhou , Dang Hong-mei &
WANG Yun-kuan
A new a robust scheme on digital
watermark based on singular value
replacement.
Invisible digital watermarking for
color images.
Decision on image watermarking
strength based on artificial neural
network.
A robust and secure watermarking scheme based on
singular values replacement
This paper proposes a blind watermarking scheme based on
Discrete Wavelet Transformation(DWT) and Singular Value
Decomposition(SVD). Singular values (SV’S) of HH band are used
to optimize the perceptual transparency and robustness constraints.
SVD helps in providing robustness but a little attention is to be paid
to their security aspect and for higher security authentication
mechanism is also provided by them. The image is first decomposed
into four frequency bands: LL, LH, HL, and HH. LL represents the
low frequency band and gives the approximate details, LH represents
the middle frequency band and gives the vertical details, HL band
represents the horizontal details and it also represents the middle
frequency, HH band represents the high frequency and it gives the
diagonal details of the image. In this proposal authors have selected
HH band to embed the watermark, as this band contains the finer
details and contributes insignificantly to the image energy. The
process for embedding the watermark is as follows:
1) Watermark W is decomposed using SVD.
ISSN: 2231-5381
The process for extracting the watermark is as follows:
Using the Haar wavelet decompose the noisy watermarked
image into four sub- bands LL, LH, HL and HH.
2) Apply SVD to the HH band.
H= UH * SH * VH T
(4)
3) Extract the singular values of the HH band
4) Construct the watermark using singular values and orthogonal
matrices UW and VW obtained using SVD of the original
watermark.
WE= UW * SH * VW T
(5)
In the watermark embedding and extraction process U and V are
orthogonal matrices and S is the diagonal matrix. The elements of S
satisfies the property:
S(1,1) > s(2,2) > S(3,3)…….S(n, n).
Cover image is not required during the extraction of the
watermark, hence it’s a blind watermarking scheme. The figure
shown below shows the extraction of the watermark.
1)
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Fig. 3. Watermarking Extraction Process
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue7- July 2013
A signature based authentication mechanism is used in this work
to provide more security. The digital signature of orthogonal matrices
can be taken as unique binary string. The proposed algorithm for the
generation of digital signature of the orthogonal matrices as follows:
1) Create 1D array by taking the sum of the columns of the
orthogonal matrices.
2) Based on the threshold map the elements of the array into binary
digits.
3) By XORing these binary digits create the digital signature of the
orthogonal matrices.
The signature should be small and embedded into LL4 and HH4
bands, to ensure recovery from at least one of the band. The
algorithm for embedding the watermark is as follows:
1) Generate the signature of N-bits for U and V matrices of the
watermarked image.
2) Using Haar wavelet decomposes the image into four sub-bands:
LL, LH, HL and HH. Decompose LL band again to 4th level.
3) Select N random coefficients from LL4 and HH4 band with the
help of secret key. Convert the integer part into binary code of L
bits.
4) Replace the nth bit of the coefficients with the signature bit and
then convert the binary code into its decimal representation.
5) Apply inverse DWT with modified LL4 and HH4 band
coefficients.
The algorithm for extraction of the digital signature is as follows:
1) Using DWT, decompose the watermarked image into four subbands: LL, HL, LH and HH with the help of Haar wavelet and
further decompose the LL band to 4th level.
2) Select n random coefficients from LL4 and HH4 band with the
help of shared secret key. Convert the integer part of the binary
coefficients into binary code of L bits.
3) Extract the nth bit from the coefficient to extract the signature.
4) Generate the signature using U and V matrices of the original
watermark at the receiver and compare it with the extracted
signature. If they match, authenticate U and V matrices and use
them in watermark estimation.
It is evident from above procedure that this proposed work in this
research paper provides more robustness and security.
Invisible digital watermarking for color images
It gives another method for embedding the watermark by using
superimposition technique using secret key. In this work the color
image is decomposed into R, G and B channels, which are treated as
separate matrices. The watermark is embedded into one of these
channels .The algorithm for embedding the watermark into the R
channel is as follows:
1) Read the RGB image 1 of size rcx.
2) Extract the red channel from three channels R, G and B.
3) Read the black and white watermark image (w) of size rcx
(same as RGB image).
4) Apply the shared secret key k that is mod2 on red channel for
encryption.
5) Now apply logical XOR on red channel of the image and result
of step 4.
6) Now apply logical or on watermark image(w) and result of step
5.
ISSN: 2231-5381
Fig. 4. Model for embedding the watermark in Red channel
1)
2)
3)
The process for extraction of the watermark is as follows:
Read the RGB image 1 of rcx.
Extract the Red channel from three channels R, G and B.
Apply the shared secret key k that is mod2 on Red channel of
watermarked image for decryption.
In this proposed work, the watermark is embedded into the
original image and this corresponding original image is not needed at
the time of extraction of the watermark. Therefore it is termed as
blind watermarking scheme.
Decision of image watermarking strength based on
artificial neural networks
This paper describes another method for embedding and extraction of
the watermark image into the original image. In this work, at first the
neural network is trained. The algorithm for embedding the
watermark is as follows:
1) The original image is divided into 8*8 pixel blocks and then
these blocks are transformed by DCT. DCT coefficients are
represented by FK(U,V) where k represents the block number
and U,V = 0,1,2,….7.
2) Select the biggest coefficient from the five coefficients (FK(0,1)
, FK(1,0) , FK(0,2) , FK(1,1) , FK(2,0)) and embed the watermark
into the coefficient according to the formula (2). In formula (2),
FK(u,v) represents the DCT transform coefficients of the
original image and F’K(u,v) represents the DCT coefficient of
the watermarked image. Using the corresponding trained ANN.
3) Maximum Watermarking strength (Tk(u,v)) is calculated. Wk
represents the watermark bits.
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue7- July 2013
4)
At the end, when all watermark bits are embedded inverse of
DCT is applied to the image blocks
F’k (u, v)= { Fk (u, v) + Wk Tk
If |Fk (u,v)| >Tk(u,v)
Fk (u,v) otherwise }
Conclusion
This research paper discusses about three dissimilar techniques for
shielding the digital content. One research paper discusses that the
watermarking strength is calculated with help of the artificial neural
network. This method helps in providing strength by making it
possible to extract the watermark from the watermarked image after
the attacks like JPEG compression, addition of various noise, etc.
Another paper affirms that the watermark is applied on one of the
three channels (R,G,B) .This approach ensures the 100% extraction
of the watermark after rotation. The third paper uses blind approach
and provides the higher degree of sturdiness against usual attacks.
This system uses signature based authentication mechanism at the
decoder to increase the level of security. The scheme discussed here
is robust against large set of attacks like cropping, rotation, filtering,
JPEG compression, Gaussian noise, print –scan etc. This proposed
scheme (DWT, SVD and signature based authentication mechanism)
is more robust as compared to the other two schemes described, as it
covers a wide range of attacks like geometrical attacks and print-scan
attacks which are not covered in the other two approaches..Hence it
can be concluded that implementing blind watermarking technique
using DWT and SVD together is better and stronger as compared to
the other techniques discussed in undertaken research papers.
REFRENCES
Fig. 5. Model for extracting the watermark from Red channel
The Watermark extraction procedure is described as follow:
At first perform DCT
transformation of the original
image(X(i,j)) and the watermarked image(X’(i,j)) and
transformed coefficients are denoted as Fk(u,v) and F’ k(u,v)
,where k represents the number k image block.
2) Find out the biggest coefficient(Fmax(u,v)) from the five
coefficients Fk(0,1) , Fk(1,0), Fk (1,1) , Fk(2,0), Fk(0,2). Compare
the maximum coefficient Fmax(u,v) with the corresponding
threshold Tk (u,v). Embed the mark if Fmax(u,v) bigger than Tk
(u,v), otherwise donot embed the mark.
3) Extract the kth watermark bit (W’k) from the DCT coefficient
into which the watermark bit is embedded using formula shown
below:
W’k = {1, if |Fk(u,v) –F’k(u,v)| ≥
0.6Tk(u,v)
0, if |Fk(u,v) –F’k(u,v)|≤
0.6Tk(u,v)}
4) The original watermark is denoted by W and the extracted
watermark is denoted by W’ .The next step is to find the
similarity between W and W’.by sim(W,W’). If it is found that
sim(W,W’) is larger than Tt , then the watermark W is thought
to be embedded into the image X’(i,j) otherwise the watermark
W is not embedded into the image X’(i,j). Here Tt is the pre-set
threshold.
Sim(W,W’)= W.W’\ √W’ ∙ √W’.
1)
ISSN: 2231-5381
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