A Novel approach of Data Hiding Using Pixel Mapping Method (PMM)

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The International Journal of Computer Science and Information Security is a monthly periodical on research articles in general computer science and information security which provides a distinctive technical perspective on novel technical research work, whether theoretical, applicable, or related to implementation. Target Audience: IT academics, university IT faculties; and business people concerned with computer science and security; industry IT departments; government departments; the financial industry; the mobile industry and the computing industry. Coverage includes: security infrastructures, network security: Internet security, content protection, cryptography, steganography and formal methods in information security; multimedia systems, software, information systems, intelligent systems, web services, data mining, wireless communication, networking and technologies, innovation technology and management. Thanks for your contributions in July 2010 issue and we are grateful to the reviewers for providing valuable comments. IJCSIS July 2010 Issue (Vol. 8, No. 4) has an acceptance rate of 36 %.

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(IJCSIS) INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND INFORMATION SECURITY , VOL. 8 , NO. 4 , JULY 2010 1

A Novel approach of Data Hiding Using Pixel
Mapping Method (PMM)
Souvik Bhattacharyya , Lalan Kumar and Gautam Sanyal

Abstract—Steganography is a process that involves hiding a mes- knowledge of steganography methodology the reader may
sage in an appropriate carrier like image or audio. The carrier can be see [14], [17].Some Steganographic model with high security
sent to a receiver without any one except the authenticated receiver features has been presented in [4], [5] and [6].Almost all
only knows existence of the information. Considerable amount of
work has been carried out by different researchers on steganography. digital ﬁle formats can be used for steganography, but the
In this work the authors propose a novel Steganographic method for image and audio ﬁles are more suitable because of their high
hiding information within the spatial domain of the gray scale image. degree of redundancy [17]. Fig. 1 below shows the different
The proposed approach works by selecting the embedding pixels categories of steganography techniques.
using some mathematical function and then ﬁnds the 8 neighborhood
of the each selected pixel and map each two bit of the secret message
in each of the neighbor pixel according to the features of that pixel
in a speciﬁed manner.This approach can be modiﬁed for mapping of
four bits of the secret message by considering more no of features of
the embedding pixel. Before embedding a checking has been done to
ﬁnd out whether the selected pixel or its neighbor lies at the boundary
of the image or not. This solution is independent of the nature of
the data to be hidden and produces a stego image with minimum Fig. 1. Types of Steganography
degradation.
A block diagram of a generic image steganographic system
Keywords—Cover Image, Pixel Mapping Method (PMM), Stego
Image. is given in Fig. 2.

I. I NTRODUCTION
TEGANOGRAPHY is the art and science of hiding infor-
S mation by embedding messages within other, seemingly
harmless messages. Steganography means “covered writing” in
Greek. As the goal of steganography is to hide the presence
of a message and to create a covert channel, it can be seen
as the complement of cryptography, whose goal is to hide the
content of a message. Another form of information hiding is
digital watermarking, which is the process that embeds data
called a watermark, tag or label into a multimedia object such
that watermark can be detected or extracted later to make an Fig. 2. Generic form of Image Steganography
assertion about the object. The object may be an image, audio,
video or text only. A famous illustration of steganography A message is embedded in a digital image (cover image)
is Simmons’ Prisoners’ Problem [16].An assumption can through an embedding algorithm, with the help of a secret key.
be made based on this model is that if both the sender The resulting stego image is transmitted over a channel to the
and receiver share some common secret information then receiver where it is processed by the extraction algorithm using
the corresponding steganography protocol is known as then the same key. During transmission the stego image, it can be
the secret key steganography where as pure steganography monitored by unauthenticated viewers who will only notice
means that there is none prior information shared by sender the transmission of an image without discovering the existence
and receiver. If the public key of the receiver is known of the hidden message. In this work a speciﬁc image based
to the sender, the steganographic protocol is called public steganographic method for gray level image has proposed. In
key steganography [2], [3] and [10].For a more thorough this method instead of embedding the secret message into the
cover image a mapping technique has been incorporated to
S. Bhattacharyya is with the Department of Computer Science and Engi- generate the stego image. This method is capable of extracting
neering, University Institute of Technology, The University of Burdwan, West
Bengal, India e-mail: (souvik.bha@gmail.com). the secret message without the presence of the cover image.
L. Kumar is with the Central Institute of Mining and Fuel Research , This paper has been organized as following sections: Sec-
Dhanbad, Jharkhand, India e-mail:(lalan.cimfr@gmail.com). tion II describes some related works, Section III deals with
G. Sanyal is with with the Department of Computer Science and En-
gineering, National Institute of Technologyy West Bengal, India e-mail: proposed method. Algorithms are discussed in Section IV
(nitgsanyal@gmail.com). and Experimental results are shown in Section V. Section VI
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contains the analysis of the results and Section VII draws the D. Data Hiding by the method proposed by Ahmad T et al.
conclusion. In this work [1] a novel Steganographic method for hiding
information within the spatial domain of the grayscale image
II. R ELATED W ORKS has been proposed. The proposed approach works by dividing
A. Data Hiding by LSB the cover into blocks of equal sizes and then embeds the
message in the edge of the block depending on the number of
Various techniques about data hiding have been proposed
ones in left four bits of the pixel.
in literatures. One of the common techniques is based on
manipulating the least-signiﬁcant-bit (LSB) [8], [9] and [13],
[15]planes by directly replacing the LSBs of the cover-image III. P ROPOSED M ETHOD
with the message bits. LSB methods typically achieve high In this section the authors propose a new method for
capacity but unfortunately LSB insertion is vulnerable to slight information hiding within the spatial domain of any gray scale
image manipulation such as cropping and compression. image.This method can be considered as the improved version
of [7].The input messages can be in any digital form, and are
B. Data Hiding by PVD often treated as a bit stream. Embedding pixels are selected
based on some mathematical function which depends on the
The pixel-value differencing (PVD) method proposed by
pixel intensity value of the seed pixel and its 8 neighbors
Wu and Tsai [18] can successfully provide both high embed-
are selected in counter clockwise direction. Before embedding
ding capacity and outstanding imperceptibility for the stego-
a checking has been done to ﬁnd out whether the selected
image. The pixel-value differencing (PVD) method segments
embedding pixels or its neighbors lies at the boundary of the
the cover image into non overlapping blocks containing two
image or not. Data embedding are done by mapping each two
connecting pixels and modiﬁes the pixel difference in each
or four bits of the secret message in each of the neighbor pixel
block (pair) for data embedding. A larger difference in the
based on some features of that pixel. Fig.5 and Fig.6 shows
original pixel values allows a greater modiﬁcation. In the
the mapping information for embedding two bits or four bits
extraction phase, the original range table is necessary. It is
respectively.
used to partition the stego-image by the same method as used
to the cover image. Based on PVD method, various approaches
have also been proposed. Among them Chang et al. [12].
proposes a new method using tri-way pixel-value differencing
which is better than original PVD method with respect to the
embedding capacity and PSNR.

C. Data Hiding by GLM
In 2004, Potdar et al.[11] proposes GLM (Gray level mod- Fig. 5. Mapping Technique for embedding of two bits
iﬁcation) technique which is used to map data by modifying
the gray level of the image pixels. Gray level modiﬁcation
Steganography is a technique to map data (not embed or hide
it) by modifying the gray level values of the image pixels.
GLM technique uses the concept of odd and even numbers
to map data within an image. It is a one-to-one mapping
between the binary data and the selected pixels in an image.
From a given image a set of pixels are selected based on a
mathematical function. The gray level values of those pixels
are examined and compared with the bit stream that is to be
mapped in the image.

Fig. 3. Data Embedding Process in GLM

Fig. 6. Mapping Technique for embedding of four bits

Extraction process starts again by selecting the same pixels
required during embedding. At the receiver side other different
Fig. 4. Data Extraction Process in GLM reverse operations has been carried out to get back the original
information.
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IV. A LGORITHMS
Let C be the original 8 bit gray scale image of size N x N
i.e. C = (Pij | 0 ≤ i < N, 0 ≤ j < N, Pij ∈ 0, 1, . . . , 255).
Let MSG be the n bit secret message represented as MSG
=(mk | 0 ≤ k < n, mk ∈ 0, 1).A seed pixel Prc can be
selected with row (r) and column (c). Next step is to ﬁnd
the 8 neighbors Pr c of the pixel Prc such that r = r + l
, c = c + l ,−1 ≤ l ≤ 1. The embedding process will be
ﬁnished when all the bits of every bytes of secret message are
mapped or embedded.
Fig. 7. A snapshot of data embedding process for two bits
A. Data Embedding Method for embedding of two bits
Algorithm of the embedding method are described as :
• Input : Cover Image(C), Message (MSG).
• Find the ﬁrst seed pixel Prc .
• count = 1.
• while (count ≤ n)
• begin (for embedding message in message surrounding a
seed pixel).
• cnt=Count number of ones of one of the Pr c of intensity
(V).
• mk =Get next msg bit.
• count = count + 1.
• mk+1 =Get next msg bit.
• count = count + 1. Fig. 8. DFA for embedding process of two bits.
• Bincvr= Binary of V.
• If(mk = 0 & mk+1 = 1)
• Bincvr(zerothbit) = 0 • BinMsg= ” ”.
• If(cnt mod 2 = 0) • Find the ﬁrst seed pixel Prc .
• Bincvr(f irstbit) = ¬Bincvr(f irstbit) • I=0.
• If(mk = 0 & mk+1 = 0) • While (count ≤ N )
• Bincvr(zerothbit) = 1 • begin (for extract message in message around a seed
• If(cnt ÷ 2 = 0) pixel).
• Bincvr(f irstbit) = ¬Bincvr(f irstbit) • Get the (First/Next) neighbor pixel Pr c .
• If(mk = 0 & mk+1 = 0) • cnt=Count number of ones of one of the Pr c of intensity
• Bincvr(zerothbit) = 0 (V).
• If(cnt mod 2 = 0) • Bincvr= Binary of V.
• Bincvr(f irstbit) = ¬Bincvr(f irstbit) • Binmsg(i)=ZerothBit of Bincvr.
• If(mk = 0 & mk+1 = 1) • count = count + 1.
• Bincvr(zerothbit) = 1 • i = i + 1.
• If(cnt mod 2 = 0) • Binmsg(i)=Enters according to One of ones in the inten-
• Bincvr(f irstbit) = ¬Bincvr(f irstbit) sity(1 for odd :0 for even).
• End • i = i + 1.
• Get the next neighbor pixel Pr c for embedding based • End.
on previous Pr c and repeat. • Get the next neighbor pixel Pr c for embedding based
• End on previous Pr c and repeat.
• Return the stego image (S). • End loop.
• Binmsg is converted back to Original message.
• Return Original Message.
B. Data Extraction Method for extraction of two bits • End.
The process of extraction proceeds by selecting those same
pixel with their neighbors. The extracting process will be
ﬁnished when all the bits of every bytes of secret message are C. Data Embedding Method for embedding four bits
extracted. Algorithm of the extraction method are described Algorithm of the embedding method are described as :
as : • Input : Cover Image(C), Message (MSG).
• Input : Stego image (S) , count. • Find the ﬁrst seed pixel Prc .
• count = count ÷ 2. • count = 1.

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• count = count ÷ 2.
• BinMsg= ” ”.
• Find the ﬁrst seed pixel Prc .
• I=0.
• While (count ≤ N )
• begin (for extract message in message around a seed
pixel).
• Get the (First/Next) neighbor pixel Pr c .
• cnt=Count number of ones of one of the Pr c of intensity
(V).
Fig. 9. A snapshot of data extracting process for extraction of two bits
• Bincvr= Binary of V.
• Binmsg(i)=3rd Bit of Bincvr from Right.
• while (count ≤ n) • i = i + 1.
• begin (for embedding message in message surrounding a • Binmsg(i)=2nd Bit of Bincvr from Right.
seed pixel). • i = i + 1.
• mk =Get next msg bit. • Binmsg(i)=ZerothBit of Bincvr.
• count = count + 1. • i = i + 1.
• Mask the 5TH bit from left with the mk in ’Bincvr’ • If (cnt mod 2 = 0) (i.e. it is even ) Binmsg(i)=0 Else
• mk+1 =Get next msg bit. Binmsg(i)=1
• count = count + 1. • Binmsg(i)=Enters according to One of ones in the inten-
• Mask the 6TH bit from left with the mk+1 in ’Bincvr’ sity(1 for odd :0 for even).
• cnt=Count number of ones of one of the Pr c of intensity • i = i + 1.
(V). • count = count + 1.
• mk+2 =Get next msg bit. • End.
• count = count + 1. • Get the next neighbor pixel Pr c for embedding based
• mk+3 =Get next msg bit. on previous Pr c and repeat.
• count = count + 1. • End loop.
• Bincvr= Binary of V. • Binmsg is converted back to Original message.
• If(mk+2 = 0 & mk+3 = 1) • Return Original Message.
• Bincvr(zerothbit) = 0 • End.
• If(cnt mod 2 = 0) One important point needs to be kept in mind that a speciﬁc
• Bincvr(f irstbit) = ¬Bincvr(f irstbit) order for selecting the neighbors of the seed pixel has to be
• If(mk+2 = 0 & mk+3 = 0) maintained for embedding / mapping process and also for the
• Bincvr(zerothbit) = 1 process of extraction other wise it would not be possible to
• If(cnt ÷ 2 = 0) retrieve the data in proper sequence. This sequence has been
• Bincvr(f irstbit) = ¬Bincvr(f irstbit) shown in Figure 8.
• If(mk+2 = 0 & mk+3 = 0)
• Bincvr(zerothbit) = 0
• If(cnt mod 2 = 0)
• Bincvr(f irstbit) = ¬Bincvr(f irstbit)
• If(mk+2 = 0 & mk+3 = 1)
• Bincvr(zerothbit) = 1
• If(cnt mod 2 = 0)
• Bincvr(f irstbit) = ¬Bincvr(f irstbit)
• End
• Get the next neighbor pixel Pr c for embedding based
on previous Pr c and repeat.
• End
Fig. 10. Sequence of data embedding
• Return the stego image (S).

D. Data Extraction Method for extracting four bits
The process of extraction proceeds by selecting those same E. Pixel Selection Method
pixel with their neighbors. The extracting process will be Random Pixel Generation for embedding message bits is de-
ﬁnished when all the bits of every bytes of secret message are pendent on the intensity value of the previous pixel selected.It
extracted. Algorithm of the extraction method are described includes a decision factor (dp) which is dependent on intensity
as : with a ﬁxed way of calculating the next pixel.The algorithm
• Input : Stego image (S) , count. for selection of pixel for embedding is described below:
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• Input: C , previous pixel position (x,y),pixel intensity
value (v).
• Consider dp (Decision Factor)=1 if (intensity ≤
80),dp=2 if (intensity ≥ 80 & ≤ 160) ,dp=3 if
(intensity > 160 & ≤ 255).
• t = x + 2 + dp
• if (t ≥ N )m = 2, n = y + 2 + dp
• else m = x + 2 + dp, n = y
• Return m and n.
• End
Fig. 13. A Segment of Cover Image with selected pixel

Fig. 11. Snapshot of Selected Pixel for embedding.

Fig. 14. A Segment of Stego Image with selected pixel with the embedded
msg segment ”I am an Indian” (two bits per pixel)

In Fig 15 shows the segment of Lena as cover image and
Fig 16 shows the same segment of Lena as stego image after
embedding the message (four bits per pixel) ”I am an Indian,
India is my country” on that segment.

Fig. 12. DFA for pixel selection.

V. E XPERIMENTAL R ESULTS
In this section the authors present the experimental results
of the proposed method based on two benchmarks techniques
to evaluate the data hiding performance based on embedding
of two bits or four bits respectively. First one is the capacity Fig. 15. A Segment of Cover Image with selected pixel
of hiding data and another one is the imperceptibility of
the stego image, also called the quality of stego image. The
quality of stego-image should be acceptable by human eyes.
The authors also present a comparative study of the proposed
methods with the existing methods like PVD,GLM and the
methods proposed by Ahmad T et al.by computing embedding
capacity, mean square error (MSE) and peak signal-to noise
ratio (PSNR).The authors also compute the normalized cross
correlation coefﬁcient for computing the similarity measure
between the cover image and stego image. In this section
experimental result of stego image are shown based on two Fig. 16. A Segment of Stego Image with selected pixel with the embedded
well known images: Lena and Pepper. In Fig 13 a segment of msg segment ”I am an Indian, India is my country” (four bits per pixel)
Lena as cover image has been shown. Fig 14 shows the same
segment of Lena as stego image after embedding the message In Fig 17 shows the image of Lena as cover and also as
(two bits per pixel) ”I am an Indian” on that segment. stego after embedding the message ”I am an Indian and I
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feel proud to an Indian.”(four bits per pixel). Fig 18 shows The PSNR is computed using the following formulae:
the same with Pepper as the image.
P SN R = 10 log10 2552 / M SE db.
A comparative study of PSNR of various methods has been
illustrated in ﬁgure 21 and ﬁgure 22 respectively.

Fig. 17. A) Cover Image B) Stego Image of Lena after embedding ”I am
an Indian and I feel proud to an Indian.”

Fig. 21. Comparison of PSNR after embedding two bits per pixel

Fig. 18. A) Cover Image B) Stego Image of Pepper after embedding ”I am
an Indian and I feel proud to an Indian.”

A comparative study of the embedding capacity with other
methods has been illustrated in ﬁgure 19 (two bits per pixel)
and ﬁgure 20 (four bits per pixel) respectively.
Fig. 22. Comparison of PSNR after embedding four bits per pixel

B. Similarity Measure
For comparing the similarity between cover image and
the stego image, the normalized cross correlation coefﬁcient
(r) has been computed. In statistics, correlation indicates the
strength and direction of a linear relationship between two
Fig. 19. Comparision of embedding capacity for two bits
random variables. The correlation coefﬁcient ρxy between two
random variables X and Y with expected values µx andµy and
standard deviations σx and σy is deﬁned as
cov(x, y) E((X − µx )(Y − µy ))
ρx,y = =
σx σy σx σy
where E is the expected value operator and cov means
covariance. The value of correlation is 1 in the case of an
increasing linear relationship, -1 in the case of a decreasing
Fig. 20. Comparision of embedding capacity for four bits linear relationship, and some value in between in all other
cases, indicating the degree of linear dependence between the
** For PVD method all the images used are of size 512x512. variables.
Cross correlation is a standard method of estimating the
A. Peak Signal to Noise Ratio (PSNR) degree to which two series are correlated. Consider two series
PSNR measures the quality of the image by comparing x(i) and y(i) where i=0,1,2,. . . ,N-1. The cross correlation r at
the original image or cover image with the stego-image, i.e. delay d is deﬁned as
it measures the percentage of the stego data to the image
percentage. The PSNR is used to evaluate the quality of the − mx)(y(i − d) − my)]
i [(x(i)
stego-image after embedding the secret message in the cover. r=
2 2
Assume a cover image C(i,j) that contains N by N pixels and i (x(i) − mx) i (y(i − d) − my)

a stego image S(i,j) where S is generated by embedding / where mx and my are the means of the corresponding series.
mapping the message bit stream. Mean squared error (MSE) The cross-correlation is used for template matching which is
of the stego image as follows: motivated through the following formula
N N
1 r= f (x, y)t(x − u, y − v)
M SE = [C(ij) − S(ij)]2
[N × N ] i=1 j=1 x
y

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where f is the image and the sum is over x, y under the the message bits are not directly embedded at the pixels of
window containing the feature t positioned at u, v. the cover image, steganalysis may be able to ﬁnd out the
Similarity measure of two images can be done with the embedded bits but can not be able to extract the original
help of normalized cross correlation generated from the above message bits.PSNR value of the proposed method (two bits
concept using the following formula: per pixel) for various sizes of the image better than compared
to other methods.
(C(i,j)−m1 )(S(i,j)−m2 )
r=
( 2 2
C(i,j)−m1 ) ( S(i,j)−m2 ) VII. C ONCLUSION

Here C is the cover image, S is the stego image,m1 is the The work dealt with the techniques for steganography as
mean pixel value of the cover image and m2 is the mean pixel related to gray scale image. A new and efﬁcient steganographic
value of stego image. It has been seen that the correlation method for embedding secret messages into images without
coefﬁcient computed here for all the images is almost one producing any major changes has been proposed. Although in
which indicates the both the cover image and stego image are this method it has been shown that each two bit or four bit
of highly correlated i.e. both of these two images are same. of the secret message has been mapped in the pixels of the
cover image,but this method can be extended to map 8 no
of bits per pixel by considering more no of features of the
embedding pixels.This method also capable of extracting the
secret message without the cover image. This approach may
be modiﬁed to work on color images also.

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Souvik Bhattacharyya received his B.E. degree
in Computer Science and Technology from B.E.
College, Shibpur, India, presently known as Bengal
Engineering and Science University (BESU) and
M.Tech degree in Computer Science and Engineer-
ing from National Institute of Technology, Durgapur,
India. Currently he is working as a Senior Lecturer
in Computer Science and Engineering Department
at University Institute of Technology, The University
of Burdwan. He has a good no of research publica-
tion in his credit. His areas of interest are Natural
Language Processing, Network Security and Image Processing.

Dr. Lalan Kumar received his Ph.D. degree from
the Indian School of Mines(ISM), Dhanbad Jhark-
hand. Joined National Informatics (NIC) Centre,
under Planning Commission of Govt. of India in
1990 and worked till 25th Nov.’02. Joined Central
Institute of Mining and Fuel Research (CIMFR) on
25th Nov.’02. Prior to joining CMRI as Scientist, he
has studied, designed, developed and implemented
many packages for the District, state and some of
the packages are running in almost all the districts
of the country. He has been appointed as a panel
expert for local governance and community engagement for the various
departments of state government. He has published more than 50 papers in
International and National Journals of repute. He is member of many advisory
board/Review committee/Chairman/Resource person of Universities/journals/
International/national Seminar cum Symposia/Institutions.Dr.Kumar has orga-
nized many International and National seminar cum exhibition time to time
and edited books.

Gautam Sanyal has received his B.E and M.Tech
degree from Regional Engineering College (REC),
Durgapur, now, National Institute of Technology
(NIT), Durgapur, West Bengal, India. He has re-
ceived Ph.D (Engg.) from Jadavpur University,
Kolkata, West Bengal, India, in the area of Robot
Vision. He possesses an experience of more than 25
years in the ﬁeld of teaching and research. He has
published nearly 40 research papers in International
and National Journals / Conferences. His current
research interests include Natural Language Process-
ing, Stochastic modeling of network trafﬁc, High Performance Computing,
Computer Vision. He is presently working as a Professor in the department
of Computer Science and Engineering and also holding the post of Dean
(Student’s Welfare) at National Institute of Technology, Durgapur, West
Bengal, India.

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