International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 1, January 2014)
Lossless Data Hiding using Histogram Modification and Hash
Encryption Scheme
Nutan Palshikar1, Prof. Sanjay Jadhav2
1
ME (Comp Engg) II Year Student, Department of Computer Engineering,MGM’s College of Engineering and Technology,
Navi Mumbai, University of Mumbai, India.
2
Assistant Professor, Department of Computer Engineering, Sarswati College of Engineering, Kharghar, Mumbai, University of
Mumbai, India.
The data hiding process links two sets of data, a set of
the embedded data and another set of the cover media data.
The relationship between these two sets of data
characterizes different applications. For instance, in covert
communications, the hidden data may often be irrelevant to
the cover media. In authentication, however, the embedded
data are closely related to the cover media. In these two
types of applications, invisibility of hidden data is an
important requirement. In most cases of data hiding, the
cover media will experience some distortion due to data
hiding and cannot be inverted back to the original media.
That is, some permanent distortion has occurred to the
cover media even after the hidden data have been extracted
out.
In some applications, such as medical diagnosis and law
enforcement, it is critical to reverse the marked media back
to the original cover media after the hidden data are
retrieved. Digital watermarking can intuitively indicate
copyright, but by removing the watermark we can also
allow users to obtain complete data, showing that digital
watermarking does not prevent authentic users of using the
data. The lossless visible watermark technology would
damage the image quality, limits its applications.
The original image of this method is non-destructive
image reproduction, overcoming the limitations of the
destruction in image quality. Some researchers have used
wavelet transform for lossless and visible watermarking,
this algorithm uses wavelet transform, which compute
more complexly, and the hidden data is limited. This
method of histogram shifting is used to mask the sub-image
information, restore more information, and attain the
purpose to limit unauthorized usage.
In recent years, several lossless data hiding techniques
have been proposed for images. Lossless embedding can
take place in the spatial domain [5], or in the transform
domain [6]. Z. Ni [5] proposed a lossless data embedding
technique, which utilizes the zero or the minimum point of
the image histogram.
A visible digital watermark issue to be addressed
includes, the amount of hidden information problems and
restore the image of edge blocking problem.
Abstract— The security of multimedia data can be done
with encryption or data hiding algorithms. This paper
attempts to undertake the study of authentication creation
and proprietorship checking using linear histogram and non
linear equalize histogram techniques with and without hash
encryption algorithm .The development of information hiding
techniques provides a solution for protecting digital media.A
lossless data hiding method based on histogram shifting and
encryption is proposed. The sub image information is
replaced by the watermark image which is the hidden
information by dividing image in 3 planes of R,G,B layer ,
then the three tier image after hiding information recorded as
R’,G’,B’. The encryption algorithm enhances the security and
offers the original data to the authenticated user and allow to
recover original image without any distortion from the
marked image after the hidden data have been extracted. An
efficient histogram shifting method that modifies the pixel
grayscale value within the range is proposed to embed data
into the image and it provide good quality of marked images.
Non-linear contrast limited enhancing Adaptive histogram
equalization method along with encryption offers the great
security and results of exhaustive experimentation using
standard input colour images demonstrate the efficiency of
Watermarking through better range of PSNR Values.
Keywords - Histogram Equalization, Hash encryption,
Reversible data hiding, Rijndael Encryption, Watermarking.
I.
INTRODUCTION
Digital watermarking is one of the ways to prove the
ownership and the authenticity of the media. There are
mainly two types of watermarking algorithms: visible
watermarking and invisible watermarking. For invisible
watermarking, the watermark should be perceptually
transparent and robustness [2] [3]. For visible
watermarking, the watermark should be perceptually
visible and robustness.
Lossless data hiding (LDH) has been widely studied as a
popular and powerful technique to protect copyright in
many sensitive scenarios, e.g., medical diagnosis, remote
sensing and law enforcement [1]. Data hiding [4] is
referred to as a process to hide data (representing some
information) into cover media.
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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 1, January 2014)
The hidden watermarking information is actually part of
the sub-image information covered by the watermark, the
amount of information depends on the format of the
original image, which makes the amount of information
hidden much higher than the amount of information in two
value image as a watermark. On the other hand, after the
restoration of the sub-image and the whole image must be
no difference visually. Finally, we must consider security
of the watermarking, illegal user can not remove the
watermark. In the absence of representation of the hidden
information, the illegal user can not access all value of the
images.
The visible digital watermarking the paper focuses on
the following points :
1. The information hidden problem, can be solved using
histogram shifting algorithm for information hiding,
2. It concentartes on restoration of image quality, so
that the covered image can be fully retrieved.
3. For greatly enhancing the security the Encryption of
the covered image is done,so that in the absence of the
key,the illegal user can not access the image
information.
The special and unique number is used just to identify
the authorised or legal user of digital information. If we
focus more on robustness then the invisibility can be poor.
So concluding that robustness with invisibility in digital
watermarking is of great use.
III. COMPARATIVE ANALYSIS O F RECENT
RESEARCHES
The techniques of Difference expansion is to It calculate
the differences of neighboring pixel values, and select some
difference values for the difference expansion and
compress message data as well as related information and
embed the result directly into the cover image. However,
not all pairs can be expanded for data hiding location map
is used to indicate whether pairs are expanded or not. A
method in this group is Barton [7] which compresses the
secret message before embedding them into the bit stream
of digital data. The second group of reversible data hiding
methods aims to explore the redundancy of pixel values in
images. The next technique of reversible data hiding
methods, to which the proposed method belongs, is based
on the concept of histogram shifting. Che-Wei Lee[15]
presented scheme of hierarchically dividing a cover image
into smaller blocks for data embedding using the histogram
shifting technique, which yields a large data hiding
capacity and results in a high stego-image quality. The key
issues of the robust LDH methods is to design a lossless
embedding mechanism to ensure the reversibility, and
construct the invariant features to achieve the robustness
against the attacks, so robust lossless data hiding (LDH)
methods is presented by Lingling An,[14]. To design the
novel robust LDH methods by introducing the more
effective and efficient invariant features, e.g., feature
points. Embedding distortion of visible watermarking is
usually larger than that of invisible watermarking. For the
application of medical and military, as they are sensitive to
distortion, Shu-Kei Yip[13] presented Pixel Value
Matching Algorithm (PVMA) and Pixel Position Shift
Algorithm (PPSA) can be used to insert the visible logo
and the original host image can be perfectly recovered after
the watermark extraction. Most of the visible watermarking
schemes do not care about ability of removing the visible
watermark. Yongjian hu [16], the user-key-dependent
removable visible watermarking system is proposed. The
user key structure decides both the embedded subset of
watermark and the host information adopted for adaptive
embedding. The neighbor-dependent embedder adjusts the
marking strength to host features and makes unauthorized
removal very difficult.
II. P ROPERTIES & REQUIREMENTS O F D IGITAL
W ATERMARKING
The various properties or can say the characteristics [20]
[21] that digital watermarking holds are:
Invisible The use of watermarking system comes to an
extinct if it distorts the cover image to such an extent that it
becomes useless, or even highly distracting. Mainly the
motive is that the watermarked image should not get easily
distinguishable from the original image even on the highest
quality equipment.
Robust The intentional attack i.e. the purposeful attempt
to distort the watermark or the unintentional attempt which
can generally occur by mistake should all be resistant.
Under unintentional attacks cropping resizing contrast
enhancement categories are included which is normally
used.
Security A watermark as being used for protection must
a secret and uncatchable code. Only authorised parties
should have the right to access the watermark. The
avoidance of unauthorised parties only help to protect the
image. Watermark because of this is treated as secure
requirement. As keys were used in cryptography, same way
watermark can be achieved. The algorithms are being used
and published to everyone to work on digital watermark. A
watermark signal is related with a unique number which is
used for embedding and extracting. This embedding and
extracting is similar to the encryption and decryption of
information.
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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 1, January 2014)
Hu et al. [23] first proposed a reversible visible
watermarking scheme by modifying one significant bit
plane of the pixels of the host image. They achieved
reversibility via losslessly hiding the compressed version of
the altered bit plane into the non-watermarked image
region. However, the embedded visible watermark with this
method appears to be somewhat blurred, and the visual
quality of the original image is significantly distorted.
Yip et al. [17] presented two lossless visible
watermarking methods based on pixel value matching and
pixel position shift, respectively. Yang et al. [18] proposed
a reversible visible watermarking scheme satisfy the
applications, in which the visible watermark is expected to
conflict copyright piracy but can be removed to losslessly
recover the original image. The performance of the
proposed method is verified, and the test results show that
the introduced scheme succeeds in preventing the
embedded watermark from illegal removal. In [19], the
visible and removable watermarking is extended in JPEG
domain. From the literature, most of the watermarks are
binary logos, and few methods of color visible removable
watermarking are proposed in JPEG domain
Tsai et al. [22] mapped the pixel values of the host
image underlying the watermark into a small range for
showing the watermark and then reversibly inserted a
reconstruction packet into the watermarked image for
perfect restoration.
B. Adaptive Histogram Equalization
Adaptive histogram equalization where you can divide the
image into several rectangular domains, compute an
equalizing histogram and modify levels so that they match
across boundaries. Depending on the nature of the non
uniformity of the image.
Adaptive histogram equalization uses the histogram
equalization mapping function supported over a certain size of
a local window to determine each enhanced density value. It
acts as a local operation. Therefore regions occupying
different gray scale ranges can be enhanced simultaneously.
The image may still lack in contrast locally. We therefore need
to apply histogram modification to each pixel based on the
histogram of pixels that are neighbors to a given pixel. This
will probably result in maximum contrast enhancement.
According to this method, we partition the given image into
blocks of suitable size and equalize the histogram of each sub
block. In order to eliminate artificial boundaries created by the
process, As in proposed system histogram modification using
adaptive histogram equalization gives better results than
linear histogram modification.
V. PROPOSED LOSSLESS V ISIBLE W ATERMARKING
In proposed method the primary image is I, with 24-bit
true color image as a original image, the image is divided
in the three color layers of the image in R,G,B planes
respectively as Ir,Ig,Ib. Hidden image first encrypted , So
as to be hidden as encrypting information W, and then
divide W into three sections of Wr ,Wg, Wb planes, after
hiding image information recorded as Ir’,Ig’,Ib’. Now
perform Embedding of encrypted information to original
image using lossless data hiding and histogram shifting
techniques. For example in the Ir layer, the process of
Hiding Wr as follows:
1. Generating the original image histogram, denoted by
h(I)
2. In the h( I ) search for H(b)=min {h(k ), k ={0, 255},
simply, we might suppose H(b)=0 . Then search for
H(a) >=L/3, a = {0, 255}. L is the hidden message
Wr’s length. We may as well set up a<b , a, b as key
record.
3. In the open interval (a, b) of gray values in Ir within
the pixel gray value increased by 1 (if b<a, reduce 1).
4. Progressive scan original image, and embed L/3 bit
information If current embedded information bit is 1,
it serves to increase the pixel gray value 1 (if b<a ,
then reduction of 1); if the current embedded
information bit is 0, then pixel gray value keep this
constant. The other pixels in the image do not need to
change.
5. Step 2 to Step 4 cycle 3 times.
IV. NONLENEAR CONTRAST ENHANCEMENT
Nonlinear contrast enhancement often involves histogram
equalizations through the use of an algorithm.
A. Histogram Equalization
Histogram equalization is one of the most useful forms
of nonlinear contrast enhancement. When an image's
histogram is equalized, all pixel values of the image are
redistributed so there are approximately an equal number of
pixels to each of the user-specified output gray-scale
classes (e.g., 32, 64, and 56).Contrast is increased at the
most populated range of brightness values of the histogram
(or "peaks"). It automatically reduces the contrast in very
light or dark parts of the image associated with the tails of a
normally distributed histogram . Histogram equalization
can also separate pixels into distinct groups, if there are few
output values over a wide range. Histogram equalization is
effective only when the original image has poor contrast to
start with, otherwise histogram equalization may degrade
the image quality to this case the adaptive histogram
equalization is improve this case.
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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 1, January 2014)
At this point, Wr has been embedded into the image Ir ,
get the watermarked image Ir’.
Embedded interval
endpoints a, b as the key saved in part by a watermark
extraction side. Encryption is employed from the point of
security .Embedding process of watermark using histogram
shifting is can be reversible by searching the range of
histogram , and varying the pixels grey value the original
image and watermark image can be recoverd.
This text uses the method of histogram shifting using
linear histogram and contrast limited equalize histogram to
mask the sub-image information and to shelter more
information, and attain the purpose to limit unauthorized
use.
VI. R IJNDAEL ALGORITHM
A. Rijndael Encryption
Rijndael the advanced encryption standard is a
symmetric block cipher.It uses the same key between the
sender and receiver to encrypt and decrypt the
message.Speed and cost make symmetric algorithms as the
algorithm of choice for encrypting large amounts of data. It
works in parallel over the whole input block.It is an iterated
block cipher with variable block length and variable key
length. More the key length more the security. The block
length and the key length can be independently specified to
128, 192 or 256 bits with the constraint that the input and
the output have the same length. Internally Rijndael
operations are performed on a two dimensional array of
bytes called the state. All the intermediate cipher and
inverse cipher results are stored in the state. This array has
four rows. The number of columns represents the data
block length to be encrypted divided by 32 and is denoted
by Nb. At the start of the cipher and inverse cipher
operations, the input block is copied into the state array; the
cipher or inverse cipher operations are then conducted on
this state array. Many mathematical operations within
Rijndael ciphertext algorithm.
Single carrier image divide into RGB layer
divide
concealed image encrypt and divide into RGB layer
divide
Histogram Calculation of original images separate layer
Calculation of encrypted message length as M/3
Search the range (a,b) in 3 layers using encrypted
Message length.
Merge (ak.bk) range of carrier image by checking value
of Watermark and change accordingly.
If embedded 1, pixel grey value increase by1, if
embedded 0 , keep constant , Save (ak,bk)
Fig. 1. Process of embedding Reversible and Hidden Information.
PLAIN TEXT
The above figure shows the overall embedding
process, skip the encryption steps for linear and non
linear histogram modification without encryption
method. It is easy to see through the embedding process
that histogram shifting is completely reversible in the case
of getting the key, when extracting the watermark the steps
as follows,
Key Round 0
Round 0
Key Round 1
Round 1
Key (ak,,bk)
……………
Find pixels whose grey value ak, ak+1 if ak
extract 0 else extract 1 till M/3 bits extracted
Extended Key
Key Round Nr-1
Round Nr - 1
Key Round Nr
Pixels grey value decreased by 1 in range
ak,bk
Last Round Nr
Reconstruct the original and watermark image
ENCRYPTED DATA
Fig. 2 Process of extracting Reversible and Hidden Information.
Fig. 3. Secure Encryption using key
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International Journal of Emerging Technology and Advanced Engineering
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(a) Data compression Techniques [7,8];
(b) Pixel-value difference expansion Techniques[9,10]; and
(c) Histogram shifting Techniques [11,12].
The techniques of the first classified method is to
compress message data as well as related information and
embed the result directly into the cover image. A method in
this group is Barton [7] which compresses the secret
message before embedding them into the bit stream of
digital data. The second group of reversible data hiding
methods aims to explore the redundancy of pixel values in
images. Tian [9] proposed a technique of pixel-value
difference expansion by performing fundamental arithmetic
operations on pairs of pixels to discover hidable space.
However, not all pairs can be expanded for data hiding. A
location map is used to indicate whether pairs are expanded
or not. An enhanced pixel-value difference expansion
method proposed by H. J. Kim[10] used a refined location
map and a new concept of expandability to achieve higher
data hiding capacities while keeping the resulting image
distortion as low as that yielded by Tian’s method [9].
The last technique of reversible data hiding methods, to
which the proposed method belongs, is based on the
concept of histogram shifting. Z. Ni[11] proposed a
reversible data hiding method which shifts slightly the part
of the histogram between the maximum point and the
minimum point to the right side by one pixel value to create
an empty bin besides the maximum point for hiding an
input message.
Advantages of this method include yielding superior
hiding capacities and providing higher qualities in stegoimages. The knowledge of the maximum point and the
minimum point of the histogram is necessary for retrieving
the hidden data and restoring the stego-image losslessly to
the original state.
INPUT
SUB_BYTES
Last Round Nr
Nr
SHIFT_ROWS
Last Round Nr
Nr
MIX_COLUMNS
Last Round Nr
Nr
ADD_ROUND KEY
OUTPUT
Fig. 4. Various steps in the encryption.
The AES algorithm’s operations are performed on a twodimensional array of bytes called the State, The array of
bytes in input is copied in the State matrix, At the end, the
State matrix is copied in the output matrix.
B. Rijndael Decryption
The decryption algorithm is not identical with the
encryption algorithm, but uses the same key schedule.There
is also a way of implementing the decryption with an
algorithm that is equivalent to the encryption algorithm
(each operation is replaced with its inverse), however, in
this case, the key schedule must be changed.
VII. LOSSLESS IMAGE D ATA H IDING
Histogram shifting is a lossless data hiding method, its
advantage is that the data embedded is large, visibility is
good, the peak signal to noise ratio is high. In the
histogram, we first find a zero point, and then a peak point.
A zero point corresponds to the grayscale value where no
pixel in the given image. A peak point corresponds to the
grayscale value which the maximum number of pixels in
the given image. The aim of finding the peak point is to
increase the embedding capacity as large as possible.The
number of bits that can be embedded into an image equals
to the number of pixels which are associated with the peak
point.
To enhance the security and to recover the image
information A reversible data hiding techniques can be
applied to restore images after the hidden data are
extracted. Such techniques can be classified as on the basis
of:
VIII. SECURITY ANALYSIS
For the illegal users, the image embedded visible
watermark has been marked with a copyright, and the
visible watermark covered a part of original image, so the
embedded watermark image are meaningless for the illegal
users, because the visible image watermark shelter
important information In the premise of an open method of
watermark embedding, only when legitimate users have the
key to eliminate visible watermark important information
can be extracted from the image. If unauthorized users
eliminate the visible watermark by sharing the image, it
also breaks the image itself and prevents the use, so as to
achieve the purpose of copyright protection.
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For the visible digital watermarking, its purpose is to
prevent unauthorized users access to the complete image
information. Only legitimate users who have the key and
password in advance can eliminate the visible watermark.
Of course, the visible watermark obscures important parts
of the image, as long as unauthorized users obtain these
important parameters information they will get whole value
of the image. That is only to obtain partially the hidden
sub-image information; all users can obtain the image
value. Visible digital watermark need to ensure that the
hidden sub-image cannot be illegally obtained, even
partially, are not permitted access. Rijndael the advanced
encryption standard is a symmetric block cipher. It uses the
same key between the sender and receiver to encrypt and
decrypt the message. Speed, cost, code length and memory
utilization make symmetric algorithms as the algorithm of
choice for encrypting large amounts of data. It works in
parallel over the whole input block.It is an iterated block
cipher with variable block length and variable key length.
More the key length more the security. In proposed system
we are using AES 128 bit , Internally, the AES algorithm’s
operations are performed on a two-dimensional array of
bytes called the State, The array of bytes in input is copied
in the State matrix, At the end, the State matrix is copied in
the output matrix
X. EXPERIMENTAL RESULTS
To verify the algorithm, we make use of standard color
grayscale images in the experiment, color and grayscale
watermark were visibly embedded to verify whether the
lossless representation of the original image. We are
comparing our watermarking and watermark removal
method using four techniques,
1) Linear Histogram modification using Encryption
2) Linear Histogram modification
without using
Encryption
3) Equalized Histogram modification using Encryption
4) Equalized Histogram modification without using
Encryption
Fig. 5. Original image
IX. LOSSLESS P ARAMETERS
We have to calculate PSNR and MSE.
Peak signal noise ratio is used to calculate the quality of
the recovered image. It is a better test since it takes the
signal strength in to consideration.
Fig. 6. Separation of Red, Green and Blue component
Red Component
255 2
PSNR = 10  log10
MSE
Where, MSE is the mean square error between the
original image and the watermark recovered image. This is
used to test whether two pictures are similar or not.
The definition of MSE is given by:
1
MSE =
M N
Green Component
M 1 N 1
 ( x
i 0 j 0
i, j
x'i , j ) 2
Where, xi,j and x'i,j are the pixel values of the original
image and the watermark recovered image, respectively. A
higher PSNR value means that the quality of lossless
restore image is closer to the original image. Our
experimental result by comparing different watermarking
techniques shows the variations in efficiency of PSNR and
MSE values.
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Blue Component
Fig. 7 Histogram of Red, Green and Blue component
c1. Watermarked image
d1. Lossless restore image
Red Component
Green Component
a2. Original Image
b2. Watermarking
Blue Component
c2. Watermarked image
d2. Lossless restore image
Fig. 9 The visible lossless Digital watermarking experiments
Fig. 6 shows the visible watermark concealing and the
host image non-destructive recovery process, in the
pictures above the images are all 512X512 standard color
images. Figure c1, c2, c3, c4 are the embedded visible
watermark images, and we can see from the experiment
that visible watermark logo watermark function can be
achieved, Figure d1, d2, d3, d4 are the lossless.The
experimental verification of digital watermark can be seen
from the non-destructive recovery performance. The
algorithm can recover the original image losslessly.
Comparison between Linear histogram and Non linear
Equalise histogram modification with and without
Encryption scheme:
Fig. 8 Equalized Histogram of Red, Green and Blue component
The image-histogram illustrates how pixels in an image
are distributed by graphing the number of pixels at each
colour intensity level.
a1. Original Image
b1. Watermarking
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TABLE I
Result Of PSNR & MSE Values of Four Techniques
Sr
No.
1.
2.
3.
4.
Type
Linear Histogram-no
Encryption
Equalizer Histogramno Encryption
Linear Histogram with Encryption
Proposed Equalizer
Histogram -with
Encryption
PSNR (dB)
MSE
20.1470
633.5541
21.1066
630.1050
23.7400
643.1080
25.6645
680.8059
XI. CONCLUSION
Based on the design rules discussed earlier, the new
image encryption scheme with histogram equalization is
designed.
This proposed scheme introduces a lossless recovery
with visible digital watermarking technology. Through
histogram shifting we achieve hiding and recovering the
information losslessely, resolving the destruction of
original images in visible digital watermark, and solving
small amount of information hidden problems. From
comparison table Non linear histogram equalization with
Encryption provides the best ratio of peak signal to noise
ratio to calculate the quality of the recovered image. For
security, in proposed scheme the encryption algorithm
offers to encrypt the data of sub image, and then spreading
a disturbance to the whole image ,so as to achieve the
purpose of protecting the image data with high security.
The visible digital watermark by hiding parts of the image
restricts the use of illegal users to protect the image. The
growth of any organization leads to enhancements, in
future the system can be enhanced according to
requirements. In order to become an effective system, these
system should provide improvement and enhancement.
Future research may be guided to more applications of the
proposed method and extensions of the method to other
data types other than bitmap images, like DCT coefficients
in JPEG images and MPEG videos. This paper can be
further enhanced based on the future trends and strategies.
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