IRJET-Data Embedding Method using Adaptive Pixel Pair Matching Algorithm

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 06 | June 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 105
Data Embedding Method Using Adaptive Pixel Pair Matching Algorithm
Mr. K. Nandha Kumar1, Mr. R. Anandan2
1PG Scholar, Department of Electronics and Communication Engineering, Gojan School of Business and Technology,
Chennai-52, India
2Assistant Professor, Department of Electronics and Communication Engineering, Gojan School of Business and
Technology,
Chennai-52, India
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Abstract- Data Hiding or steganography has been an
important communication network since people started
communication in writing. The main aim in steganography is
to hide a secret message within cover media in such a way that
an observer cannot detect the presence of contents of the
hidden message. Cover images/video are original images
without secret data and after embedding secret data they are
called as stego images. steganography and cryptography are
the parts in digital security the obvious advantage of
steganography over cryptography is that messages do not
attract attention to themselves, to messengers, or to recipients.
The progress in steganography has also led to many serious
problems such as hacking, compression, reformat, etc.
steganography finds its role in attempting to address these
growing concerns, with the use of steganography techniques, it
is possible to hide secret information within images, audio and
video files which are statistically undetectable. This present
work proposes a new data embedding technique which uses
two pixels sequentially one after another which is known as
pixel pair matching (PPM) technique. The two pixel is then
replaced by the searched coordinate to store the digit. The
APPM (Adaptive Pixel Pair Matching) method offers lower
distortion than DE (Diamond Encoding) by providing more
compact neighborhood sets and allowing embedded digits in
any notational system.
Key Words—Adaptive pixel pair matching (APPM),
diamond encoding (DE), exploiting modification
direction (EMD), least significant bit (LSB), optimal pixel
adjustment process (OPAP), pixel pair matching (PPM).
1. INTRODUCTION
As need of Internet-based applications is highly increased,
so it is required to use the secrecy in communication. To
achieve this goal, there are mainly three techniques are
available, cryptography, watermarking and steganography.
Steganography is the art of embedding the information
through original object in such a behavior that the
continuation of the message is unknown. The term
steganography is coming from Greek word Steganos, which
means, “Covered Writing”. The original objects can be
referred to as covered/carrier objects. After inserting the
secret message in to the cover image it is called as stego
image. A stego key is used for hiding. Steganography is
different from cryptography. The main objective of
cryptography is to secure communications by using
encryption techniques. But steganography techniques are
used to hide the messages, which makes difficult for a third
party / person to find out the message. Watermarking and
fingerprinting related to steganography are basically used
for academic property protection.
2. DIFFERENT KINDS OF STEGANOGRAPHY
The five main categories of file formats that can be used for
steganography are:
2.1 Text Steganography
This method is used to hide a secret message in a text
message for that number of tabs, white spaces, and capital
letters, just like Morse code is used. In earlier day this
technique is in very much boom but after booming of
Internet and different type of digital file formats its
importance gets decreased.
2.2 Image Steganography
Digital images are widely used over the internet as well as
that are also popular. Now using this phenomenon, the
digital images can be used as a cover images/objects for the
steganography. In this technique a secret message is hided in
a digital image through an embedding algorithm with the
help of private key to generate a stego image.
2.3Audio Steganography
Audio stenography is another type of steganography in
which the properties of the human ear is considered to hide
information. An audible, sound can be made inaudible in the
presence of another louder audible sound. Audio
steganography uses only digital audio formats such as
WAVE, MIDI, AVI MPEG or etc.
2.4Video Steganography
Video Steganography is a technique used to hide any kind of
files or information into digital video format. Here video is
used as carrier for hidden information. Video steganography

uses following types of video files such as H.264, Mp4, MPEG,
AVI or other video formats.
2.5Protocol Steganography
The term protocol steganography is to embedding
information within network protocols such as TCP/IP. We
can hide information in the header of a TCP/IP packet in
some fields that can be either optional or are never used. The
protocol steganography uses the TCP, UDP, IP network
protocols for data hiding.
3. STEGANOGRAPHIC TECHNIQUES
Digital image steganography techniques can be divided into
following domains:
3.1 Spatial Domain Techniques
There are many versions present in spatial steganography
that all are related to make changes directly with some bits
of the digital image pixel values to hide data. Least significant
bit (LSB) - based steganography is one of the simplest
techniques/method that is used to hide a secret message in
digital image. In this technique the pixel value of least
significant bit (LSB) are get replaced with bits of secret
message and all this is done without introducing many
perceptible distortions. The embedding of message bits can
be done either sequentially or randomly. In spatial domain
following techniques comes these are as follows such as LSB
substitution/replacement, LSB matching, Matrix embedding
and Pixel value, differencing etc.
3.2 Transform Domain Techniques
This is a more complex technique of hiding information in an
image. In this various transformation algorithms are used to
hide data behind the image [4]. This technique also is termed
as a domain of embedding techniques. In this technique a
number of algorithms are exists. Most of the strong
steganographic systems work in the transform domain
because the process of embedding data in the frequency
domain of a signal is much stronger than any other domain
such as time or etc. Transform domain techniques are more
advantageous than spatial domain because it hides
information in such parts of image that are less exposed to
image processing, cropping and compression. Transform
domain techniques are mainly classified as follows:
1. Discrete Fourier transformation technique (DFT).
2. Discrete cosine transformation technique (DCT).
3. Discrete Wavelet transformation technique (DWT).
4. Lossless or reversible method (DCT)
5. Embedding in coefficient bits
3.3 Distortion Techniques
This technique works on the principle of differences between
the cover image and the stego image and for that it needs to
keep a track of cover image. While embedding the secret
message by this technique the encoder adds a sequence of
changes to the cover image i.e. way in which and how/where
the secret message get embedded. For this process the
difference in the signal distortion is considered. But before
the embedding a secret message into the cover image it has
to encode it for that the encoder chooses the pixels of cover
image pseudo-randomly and changes that pixel bit with
message bit in a such manner the statistical properties of the
image are not get affected. However, this method has one
drawback as cover image should never be used more than
once. As well as an attacker can easily tampers the stego-
image by cropping, scaling or rotating.
Fig.1. Block Diagram of Steganography
Fig.2. Cover and Stego Image

3.4 Masking and filtering
This technique has resemblance with the technique of paper
watermark. In this technique information is get concealed by
marking an image for that purpose it uses the noise levels of
the cover images. The main advantage of this technique is
that the hidden message is more integral to the cover image
and watermarking techniques can be easily applied as well
as there is no fear of image destruction. This method has one
more advantage as it is much more robust than LSB
replacement with respect to comparison made based on the
following the categories such as compression and the
information is hidden in the visible parts of the image. This
technique also has the disadvantage as it only works on gray
scale images.
4. LITERATURE SURVEY
This section provides the knowledge of different data hiding
techniques are used to hide the data. These are as follows:
4.1 Pixel Value Differencing (PVD)
In Paper “An Image Steganographic Scheme Based on Pixel-
Value Differencing and LSB Replacement Methods” author
proposed a Pixel value difference (PVD) and simple least
significant bits scheme are used to achieve adaptive least
significant bits data embedding. In pixel value differencing
(PVD) where the size of the hidden data bits can be
anticipated by difference between the two consecutive pixels
in cover image using simple relationship between two pixels.
PVD method provides a better imperceptibility by calculating
the difference of two consecutive pixels which determine the
depth of the embedded bits. This method hides large data
with the help of LSB substitution at edge area of image and
uses the PVD for smooth region of image to hide the data.
Though this technique provides larger capacity but has low
visual quality as well as this method is complex.
4.2 LSB Substitution
In paper “Hiding Data in Images by Simple LSB Substitution”
authors proposed an LSB substitution for hiding the data
into the image. To achieve better visual quality of stego-
image it takes care of noise sensitive area for embedding.
This method intelligently differentiates normal texture and
edges area of an image as well as it takes the advantage of
these areas for the embedding. This method analyses the
different LSB values as well as edges, texture masking and
brightness of the cover image to calculate the number of k-
bit LSB for secret data embedding. It also utilizes the pixel
adjustment method for better stego-image visual quality
through LSB substitution method. Optimal pixel adjustment
process is also used to generate the stego-image which is
obtained by the simple LSB substitution method. The
proposed method also termed as OPAP (Optimal Pixel
Adjustment Process). The overall result shows a good high
hidden capacity with high image quality of the stego-image
can be greatly improved with low extra computational
complexity. The main shortcoming of this technique is the
worst mean-square-error between the stego-image and the
cover image is derived.
4.3 Exploiting Modification Direction (EMD)
In paper “Efficient Steganographic Embedding by Exploiting
Modification Direction” author provides a new approach to
data hiding scheme by introducing a novel method of
steganographic embedding in digital images is described, in
which each secret digit in a (2n+1) notational system is
carried and hide by n pixels of the cover image. In this
method at most only one pixel is increased or decreased by
1. It is not suitable for applications that requiring high
payload is the main shortcoming of this technique.
4.4 Diamond Encoding (DE)
In paper “A Novel Image Data Hiding Scheme with Diamond
Encoding” author provides a new approach to data hiding
scheme by introducing Diamond Encoding. In this technique,
first the process is portioning and embedding of the cover
image into non-overlapping blocks of two consecutive pixels.
Then it transforms the secret messages into a series of digits
which are equivalent to those blocks. Afterward the diamond
encoding technique is applied on those blocks to calculate
the diamond characteristic values i.e. DCV to hide/concealed
secret B-ary digits into the diamond characteristic values.
After that the diamond characteristic value is gets modified
by secret message digit and which can be done by adjusting
pixel values in blocks. The main shortcoming of this
technique is that it suffers from higher distortion for various
lower payload with lower image quality and can be attacked
by capacity in terms of payload and performance that can be
improved. The PPM-based method, suppose a digit TB is to
be concealed. The range of TB is between 0 and B-1, and a
coordinate (p’, q’) in Ø (p, q) has to be found such that f (p’,
q’) = TB. [2] Therefore, the range of (p, q) must be integers
between 0 and B-1, and each integer must occur at least
once. In addition, to reduce the distortion, the number of
coordinates in Ø (p, q) should be as small aspossible. The
best PPM method shall satisfy the following three
requirements:
1) There are exactly B coordinates in Ø (p, q).
2) The values of extraction function in these coordinates are
mutually exclusive.
3) The design of Ø (p, q) and f (p, q) should be capable of
embedding digits in any notational system so that the best
can be selected to achieve lower embedding distortion. The
definitions of Ø (p, q) and f (p, q) significantly affect the stego
image quality. The designs of Ø (p, q) and f (p, q) have to
fulfill the following requirements: All values have to be

mutually exclusive and the summation of the squared
distances between all coordinates in Ø (p, q) and f (p, q) has
to be the smallest. This is because, during embedding, (p, q)
is replaced by one of the coordinates in Ø (p, q). Suppose
there are B coordinates in Ø (p, q), i.e., digits in a B-ary
notational system are to be concealed, and the probability of
replacing (p, q) by one of the coordinates in Ø (p, q) is
equivalent. The averaged MSE can be obtained by averaging
the summation of the squared distance between and other
coordinates in Ø (p, q).
5 PROPOSED SECURED APPM METHODOLOGY
5.1 Encryption and Decryption Procedure
Before embedding or hiding the secret message in to the
cover image it is inputted to encryptor for the encryption. As
well as after the extraction process again this secret message
is passed to decryptor for decryption and then the original
message is obtain. The advantage of this methodology is that
it protects the secret message from the attacker under the
steganalysis attack. In addition to English language, this
secured APPM technique is also able to hide data provided in
different languages such as Marathi, Hindi etc.
5.2 Embedding Procedure
The embedding module accepts the encrypted secret
message as input from encryptor module and process on it to
embed that encrypted secret message in to cover image as a
steganography for the secret communication. The output of
this embedding module is the stego image which contains a
secret message. Suppose the cover image is of size U × U and
M is the message bits to be concealed. The size of is T is |T|
and key Er.
Fig.3. Proposed System
5.3 Extraction Procedure
The extraction module accepts the stego image which
contains secret message as an input from the embedding
module. The extraction module process on that input and
generate cover image and encrypted secret message. This
encrypted secret message is then pass to decryptor module
to generate plain secret message. To extract the embedded
message digits, pixel pairs are scanned in the same order as
in the embedding procedure. The message digits which we
were embedded in the previous phase is considered /
forwarded as the parameter values of extraction function
which is got from the scanned pixel pairs.
1) Construct the embedding sequence j using the key Hr.
2) Select two pixels (p’, q’) according to the embedding
sequence j
3) Calculate f (p’, q’) and the result is the embedded digit.
4) Finally, the message bits T can be obtained by converting
the extracted message digits into a binary bit stream.
5.4 Digital Watermark
In digital watermark module a digital watermark is
implemented. A digital watermark is a kind of marker which
is secretly embedded in a noise-tolerant signal such as
image, video, audio and text etc. Digital watermarks may be
used to verify the authenticity; integrity of the carrier signal
as well as it shows the identity of its creator/owner. It gives
a recipient reason to believe that the message was created by
a known sender, such that the sender cannot deny having
sent the message (Authentication and non repudiation) and
that the message was not altered in transit (i.e. to maintain
integrity). As well as watermark gives integrity of message.
The digital watermark gives authentication for secured
communication. In this system message received from
sender is in authenticated form and with security in
communication. Due to this sender cannot deny about
message sent by him and this also assures that message was
not altered in transit.
6 RESULTS AND ANALYSIS
This section shows the various types of results of Secured
APPM technique with different parameters. These results
show improvement over previous APPM techniques with
respect to Mean Square Error (MSE). The cover and stego
images which do not show any artifacts after applying
Secured APPM technique to the cover image to hide the
secret message in to it.

Fig.4 Cover Image and Stego Images
(a) Cover Image (b) Stego Image
The Table 1 shows the comparison of MSE between the
secured APPM technique with the previous technique such
as APPM, DE, LSB and OPAP etc
Table 1 MSE comparison of proposed method (Secured
APPM) with previous APPM method
bpp B C
B
APPM Secured
APPM
MSE
Improvement
MSE MSE
1 4 2 0.375 0.25 0.125
1.161 5 2 0.4 0.1666666 0.23333
1.850 13 5 1.077 1.0660000 0.011
2.679 41 6 3.341 3.1666666 0.17433
3 64 14 5.203 4.8095238 0.39347
3.205 85 10 6.847 6.8333333 0.01366
3.410 113 31 9.071 8.6021505 0.46884
4 256 92 20.518 19.550724 0.96727
The Table 1 shows the improvement of Mean Square Error
(MSE) as compared to previous APPM data hiding technique
Fig.5. Payload - MSE Relationship Comparison of Various
PPM-Based Methods
Table 2 MSE Comparison for various types of images
CB = 7, B = 32, payload = 2.480 bpp
Images
APPM
Secured
APPM MSE
Improvement
MSE MSE
Lenna 2.604 2.466 0.138
Jet 2.609 2.466 0.143
Boat 2.598 2.59 0.008
House 2.6 2.53 0.07
Elaine 2.582 2.49 0.092
Average
MSE
2.5986 2.5084 0.0902
CB = 3, B = 9, payload = 1.526 bpp
Images
APPM
Secured
APPM MSE
Improvement
MSE MSE
Lenna 0.642 0.54 0.102
Jet 0.648 0.54 0.108
Boat 0.64 0.53 0.11
(a) (b)
(a) (b)

House 0.632 0.57 0.062
Elaine 0.638 0.57 0.068
Average
MSE
0.64 0.55 0.09
The Table 2 shows the performance improvement of Secured
APPM technique over the APPM technique. This table shows
the performance improvement of MSE and with payload of
3.815 bpp, 2.480 bpp and 1.526 bpp.
Embedding Procedure Form
Extraction Procedure Form
7 PERFORMANCES AND SECURITY ANALYSIS
In this section, we analyze the security of Secured APPM
under statistical steganalysis schemes, including the HVDH
scheme. The HVDH [15] scheme is used to detect the
presence of hiding message according to the distance
between vertical and horizontal histograms. [15] The
proposed a detection method based on the statistical
analysis of histogram differences. Zhao et al. [15] observed
that for many pair wise embedding methods, the difference
between the horizontal difference histograms and vertical
difference histograms are significantly altered. Zhao et al.
[15] use the distance between Hh and Hv as a statistical
detector to detect the abnormality of histogram. [15] The
distance is defined as
T is a predefined threshold. A larger D indicates that Hh and
Hv have larger differences and thus, the image is likely to
have messages embedded. [2] The fig.8 shows the security
analysis and comparison of histogram of stego and cover
images. From these figures we can say that the histogram of
cover and stego images are equal. To detect the presence of
secret message we employ the security analysis and
steganalysis attack on these images but the results produced
from them shows that the Secured APPM technique is secure
under the detection of some well lknown steganalysis
techniques.
(c) Stego Image (d) Histogram of Stego image
Fig.8. Security Analysis and Comparison of Cover and Stego
Images
(a) Cover image (b) Histogram of Cover image

8 CONCLUSION
This paper proposed a simple and efficient data embedding
method named as Secured APPM based on APPM. In that two
pixels are used as an embedding unit and a specially
designed compact neighborhood set is used to embed secret
message digits in to a smallest possible notational system by
allowing users to select digits in any notational system for
the data embedding. The proposed method not only resolves
the low-payload problem in EMD, but also offers smaller
MSE than OPAP, DE and APPM. It also provides a better
image quality because Secured APPM does not produce any
artifacts in stego images. The steganalysis results of stego
images are similar to those of the cover images, which offer a
secure communication under adjustable embedding capacity.
It also contains additional features such as digital watermark
and encryption of secret messages for the provision of more
security. The secured APPM technique is also able to hide all
different types of data provided to it in languages like
English, Marathi, and Hindi etc. As well as this technique is
secure under the detection of some well-known steganalysis
techniques. All these various features made a Secured APPM
technique a straightforward, economical embedding method
for the data hiding. In future Secured APPM may have a
chance to increase the capacity of data embedding in the
cover images of it. Also there may be a chance of little
improvement of MSE in it and chance to provide more
security.
ACKNOWLEDGMENT
Sincerely thank the all experts who have contributed
towards development of this paper.
REFERENCE
[1] W. Zhang, X. Zhang, and S. Wang, “A double layered plus-
minus one data embedding scheme,” IEEE Signal
Process. Lett, vol. 14, no. 11, pp. 848–851, Nov. 2007.
[2] Wien Hong and Tung-Shou Chen, “A Novel Data
Embedding Method Using Adaptive Pixel Pair Matching”,
IEEE Transactions on Information Forensics and
Security, Vol. 7, No. 1, February 2012.
[3] R.M. Chao, H. C. Wu, C. C. Lee, and Y. P. Chu, “A novel
image data hiding scheme with diamond encoding,”
EURASIP J. Inf. Security, vol. 2009, 2009, DOI:
10.1155/2009/658047, Article ID 658047.
[4] Swapnil S. Thakare and Niranjan L. Bhale, “A Review of
Digital Image Steganography Techniques”, International
Journal of Advanced Research in Computer Science and
Software Engineering, Volume 4, Issue 6, June 2014, pp.
465-471, ISSN: 2277 128X.
[5] M. Lakshmi Prasanna and Sk. Mahaboob Basha,
“Extended Adaptive Pixel Pair Matching”, International
Journal of Engineering Research and Applications, Vol.
3, Issue 3, May-Jun 2013, pp.1484-1490, ISSN: 2248-
9622.
[6] Pratap Chandra Mandal, “Modern Steganographic
technique: A Survey”, International Journal of Computer
Science & Engineering Technology (IJCSET) Vol. 3 No. 9
Sep 2012.
[7] Mehdi Hussain and Mureed Hussain, “A Survey of Image
Steganography Techniques”, International Journal of
Advanced Science and Technology Vol. 54, May, 2013
[8] Hsien-Chu Wu Na-I Wu Chwei-Shyong Tsai MinShiang
Hwang, “An Image Steganographic Scheme Based on
Pixel-Value Differencing and LSB Replacement Methods”,
National Science Council, Taiwan, Nov 2004.
[9] X. Zhang and S. Wang, “Efficient steganographic
embedding by exploiting modification direction,” IEEE
Commun. Lett. vol. 10, no. 11, pp. 781–783, Nov. 2006.
[10]K. Chan, L. M. Cheng, “Hiding data in images by simple
LSB substitution”, Pattern Recognition, vol. 37, no: 3, pp:
469–474, 2004.
[11]J. Mielikainen, “LSB matching revisited,” IEEE Signal
Process. Lett. vol. 13, no. 5, pp. 285–287, May 2006.
[12]J. Fridrich and D. Soukal, “Matrix embedding for large
payloads,” IEEE Trans. Inf. Forensics Security, vol. 1, no.
3, pp. 390–394, Sep. 2006.
[13]W. Hong and T. S. Chen, “Reversible data embedding for
high quality images using interpolation and reference
pixel distribution mechanism,” J. Vis. Commun. Image
Represent, Vol: 22, no. 2, pp. 131– 140, 2011.
[14]M. Tanvir Parvez and A. Abdul-Aziz Gutub, “RGB
Intensity Based Variable-Bits Image Steganography”,
IEEE Asia-Pacific Services Computing Conference, pp.
1322-1327. 2008.
[15]H. Zhao, H. Wang, and M. K. Khan, “Statistical analysis of
several reversible data hiding algorithms,” in Proc.
Multimedia Tools and Applications, 2009, DOI:
10.1007/s11042-009-0380

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