International Journal of Engineering Trends and Technology (IJETT) – Volume 14 Number 3 – Aug 2014
Novel and Secure Image Steganography Using
OPVD with High Capacity
V. Navya1
T.Ravi Kumar Naidu2
T.V.S. Gowtham Prasad3
PG Student, Dept. of ECE, SVEC, Tirupati, Andhra Pradesh1
Assistant Professor, Dept. of ECE, SVEC, Tirupati, Andhra Pradesh2
Assistant Professor, Dept. of ECE, SVEC, Tirupati, Andhra Pradesh3
Abstract--This paper proposes a new high payload
capacity
steganographic
(octonary
pixel
value
scheme
using
differencing)
OPVD
with
high
statistical un-detectability and perceptual quality.
The novel algorithm performs pairing a pixel with all
of its neighbors in all the eight directions to increase
the embedding capacity and the number of bits
Steganography techniques
domain
and
frequency
domain
are
spatial
techniques.
Frequency domain techniques are more complex
than
spatial
domain
techniques.
Many
steganographic methods are proposed in spatial
domain. The basic steganography technique in
embedded in each pixel is based on the nature of its
spatial domain is LSB replacement[3], where the
region. Experimental results show that the proposed
least significant bit in each pixel of image is
algorithm is secure, with high capacity and very low
replaced with the secret message bit. The change in
degradation. Also, the performance of the method is
LSB bit results un-detectability and less hiding
compared with PVD and tri way PVD steganography
capacity.
techniques.
The
quality of
the
steganography
measured based on PSNR and maximum hiding
capacity.
Keywords--Image
steganography,
hiding
capacity,
Digital
get the hiding capacity in those pixels. It hides
INTRODUCTION:
images
are
the
numeric
representation of a two-dimensional image which
led to the development of many steganographic
techniques. A steganographic method is judged by
its hiding capacity(the maximum size of secret data
that is embedded inside an image) and statistical
un-detectability(stego image is more similar to that
of original
image).
scheme is PVD (pixel value differencing)[1]. It
calculates difference value between two pixels to
Octonary PVD, Un-detectability.
I.
The other category of steganography
A good steganographic
technique sustains both maximum hiding capacity
and un-detectability. Typically, with the increase in
more information in edge regions than smooth
regions, which results good hiding capacity with
reduced artifacts. Tri way PVD is extension of
PVD technique which increase the hiding capacity
by calculating the difference of pixels in three
directions.
A novel steganography technique is
proposed which is extension of PVD to enlarge the
hiding capacity and to sustain the un-detectability
concurrently.
the secret message bits introduces more artifacts in
stego image. Practical applications demand the two
properties viz., un-detectability and high payload
capacity to be carefully considered to design a
steganographic algorithm.
II.
PROPOSED METHOD:
The block diagram of proposed method is
shown in Fig.1. In the embedding side, the cover
image is divided into non-overlapping blocks of
some size and each block is shifted by an angle of
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International Journal of Engineering Trends and Technology (IJETT) – Volume 14 Number 3 – Aug 2014
some degrees ɵ to obtain the shifted image. That
shifted image is used to hide the secret data to
provide
high
security
which
improves
the
perceptual quality. Again the same shifted image is
divided into non-overlapping blocks of size 3*3,
each with nine pixels in pre-processing phase. The
eight neighboring pixels form eight pixel pairs with
the center pixel. The proposed approach hide more
number of bits in edge areas than smooth
regions[5,6] so first the regions are identified to
hide the data in region identification phase.
Fig.1. Block diagram of proposed system (a)Data embedding (b)
Data extraction
Subsequently, the number of hiding bits in each
pixel pair is determined by referring range table.
As shown in Fig.2, each hiding unit has nine pixels
After utilizing all the edge regions, smooth regions
P(x, y), P(x-1,y-1), P(x-1,y), P(x-1,y+1),P(x, y-1),
are used to hide the data. The range table is
P(x, y+1), P(x+1, y-1), P(x+1, y),P(x+1,y+1) where
constructed by dividing [0 255] range into different
x and y are pixel locations in an image. Let P(x, y)
levels. Data is hidden based on OPVD algorithm.
is a center pixel with eight neighboring pixels
The exceeded pixel values are adjusted to get the
which are denoted as P0, P1, P2, P3, P4, P5, P6,
pixel values within the range in pixel readjustment
P7, P8.
phase. Finally the post processing phase is used to
obtain the stego image. In extraction, almost
P(x-1,y-1)
P(x, y-1)
P(x+1, y-1)
P(x-1,y)
P(x, y)
P(x+1, y)
P(x-1,y+1)
P(x, y+1)
P(x+1,y+1)
similar blocks are used to retrieve the hidden
information. The details of the data embedding and
data
extraction
procedure
of
our
proposed
algorithm are as follows.
A. Data embedding:
Preprocessing phase: The cover image I is divided
Range
Hiding capacity in
bits
into non-overlapping blocks of size R*R, the size
of block provides stego_key1. Each block is shifted
[0 7]
3
by an angle of {00, 900, 1800, 2700} which provides
[8 15]
3
stego_key2. While segmenting the image no pixel
[16 31]
4
is left behind for security reasons, for that the block
[32 63]
5
size must be even. If some pixels are left then that
[64 127]
6
pixels must be unhandled.
[128 255]
7
Fig.2 (a) Hiding Unit HU (b) range table RT
The resulting image denoted as RI is again
split into 3*3 non-overlapping blocks. Each block
Region identification phase: The edge regions are
is called as hiding unit HU. Hiding unit is shown in
first identified to hide the data because edge
Fig. 2(a).
regions hold more number of bits than smooth
regions to improve perceptual quality and security.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 14 Number 3 – Aug 2014
Range table construction phase: The range table
provides
adaptive
hiding
capacity
and
Case
is
constructed by calculating the difference value of
pixels with their center pixel. Let the center pixel is
(
Case
upper and lower bounds in range table are
and
)
⌈ ⌉
(
⌊ ⌋)
8:
(
(i=1,2,….8). The
P0 and neighboring pixels are
7:
)
⌊ ⌋
(
⌈ ⌉)
Where
and
then the difference value is calculated as
are the new pixel values after embedding
the secret data. If the new pixel values fall out of
The hiding capacity of pixel pairs is obtained by
⌊
The width
⌋
boundary then the values are adjusted to have with
in the boundary values.
Readjustment phase: After embedding the secret
is defined as
data, the center pixel value in each hiding unit is
Hi number of bits can be hidden inside each pixel
changed eight times let the new center pixel values
pair. Read Hi number of bits from binary data and
are
transform those bits to decimal value Bi. If the
center pixel need to round off to single value to
difference value
is large then those pixels hide
form a 3*3 block accordingly the neighboring
more number of bits. The range table for proposed
pixels are changed. The center pixel value is round
method is shown in Fig. 2(b).
off as
OPVD embedding phase: Embed
remainder is to be calculated to find the new values
)
. The
)
The neighboring pixel values are defined as
where
. Now
hiding units with secret information are formed
.The remainder is obtained as
(
((∑
number of bits
in hiding unit by altering the pixels P0 and Pi. The
of P0 and
and new neighboring pixels are
with the changed pixel values.
)
Where
Post processing phase: After embedding the secret
The optimal approach to alter pixels is as
data, all hiding units are combined to form stego
follows[2]
image. Finally the stego image is divided into
Case
blocks of same size as in preprocessing phase and
1:
(
Case
)
(
⌈ ⌉
⌊ ⌋)
degrees as first phase but in opposite direction so
2:
that the stego image is similar to that of cover
(
Case
)
(
⌊ ⌋
⌈ ⌉)
image with secret data.
B. Data extraction:
3:
(
each of that block is shifted by an angle of same
)
(
⌊ ⌋
⌈ ⌉)
The stego image is divided into blocks and
each block is shifted by an angle of some degrees
Case
4:
(
Case
(
⌈ ⌉
⌊ ⌋)
5:
(
Case
which is same as that of data embedding, again is
)
range table is considered to extract correct
)
(
⌈ ⌉
⌊ ⌋)
information. In OPVD extraction phase, the
difference value is calculated between center pixel
6:
(
split into 3*3 blocks called hiding units. The same
)
(
ISSN: 2231-5381
⌊ ⌋
⌈ ⌉)
and its neighboring pixels then that value is
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International Journal of Engineering Trends and Technology (IJETT) – Volume 14 Number 3 – Aug 2014
represented as binary string. Finally the binary data
is converted to text to know the secret information.
III.
RESULTS AND DISCUSSIONS:
Original
OPVD
PVD
TPVD
image
image
image
image
Cover image: Baby.jpg
Three steganography techniques using
PVD, tri way PVD[4] and proposed method have
been implemented in MATLAB which are applied
to baby.jpg,scenery.jpg,Light.jpg images. Fig. 4
Cover image: Scenery.jpg
represents the text file chosen for hiding in cover
image. In the Fig.5 stego images were compared
with each other and with original cover image. In
Fig 6 the histograms of each image is compared.
Cover image: Light.jpg
Fig. 6.histogram analysis for PVD, TPVD and OPVD methods
Cover image: Baby.jpg
Quality
OPVD
PVD
Tri way
parameters
Fig. 4 121 KB data hiding in the cover image
OPVD
PVD
PSNR
45.17
48.91
46.11
Maximum
359344
251538
323406
capacity(bits)
Stego images
Original
PVD
Triway PVD
images
Cover image: Scenery.jpg
PSNR
59.2336
64.9457
60.5158
Maximum
989256
800248
912442
capacity(bits)
Cover image: Light.jpg
PSNR
58.847
62.807
59.5247
Maximum
926224
884232
850324
capacity(bits)
Table. 1. Quality parameters analysis
Stego quality analysis and comparison have been
done by observing Table 1. From PSNR and
Maximum capacity observations it is clear that
capacity is maximum for OPVD method compared
Fig.5 Stego images obtained by PVD, TPVD and proposed
method OPVD
to PVD and TPVD and is also sustains the image
quality.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 14 Number 3 – Aug 2014
IV.
CONCLUSION
BIOGRAPHY
In this paper a new steganography method OPVD
along with PVD and Tri way PVD method was
proposed,
implemented
and
analyzed.
The
proposed method hides data by dividing image into
hiding units and in each hiding unit the difference
is calculated with neighboring pixels by the center
pixel to hide the data. Quality measures were done
using PSNR and maximum capacity. From the
table and fig. we conclude that the perceptual
Ms. V.Navya, P.G Student,
Dept of ECE, SreeVidyanikethan Engineering
College, A. Rangampet, Tirupati received B.Tech
in Electronics and Communication Engineering
from YITS, Tirupati Interesting Areas Digital
Signal Processing, Image Processing, Embedded
Systems, Digital Communications
quality is good with hiding of maximum data in
OPVD than PVD and tri way PVD.
REFERENCES
[1] J. K. Mandal and Debashis Das, “Colour Image
Steganography Based on Pixel Value Differencing in Spatial
Domain”, International Journal of Information Sciences and
Techniques (IJIST), Vol.2, No.4, July. 2012
[2] C. Balasubramanian , S. Selvakumar& S. Geetha “High
payload image steganography with reduced distortion using
octonary pixel pairing scheme” Springer Science+Business
Media New York 2013
[3] Chan C-K, Cheng LM (2004) Hiding data in images by
simple LSB substitution. Pattern Recogn 37(3):469–474, 4
Mr. T .Ravi Kumar Naidu
Assistant
Professor,
Dept
of
ECE,
SreeVidyanikethan Engineering College, A.
Rangampet, Tirupati received B.Tech in
Electronics and Communication Engineering from
SVPCET, Puttur and M.Tech received from HIET
affiliated to JNTUH, Hyderabad. Interesting Areas
Digital Signal Processing, Array Signal Processing,
Image Processing, Video Surveillance, Embedded
Systems, Digital Communications
[4] Chang K-C, Chang C-P,Huang PS, Tu T-M (2008) A novel
image
steganographic
method
using tri-way
pixel-value
differencing. J Multimedia 37(2):44, 6
[5] Zhang X, Wang S (2004) Vulnerability of pixel-value
differencing
steganography
to
histogram
analysis
and
modification for enhanced security. Pattern RecognitLett
25:331–339, 46
[6] Yang CH, Weng CY, Wang SJ, Sun HM (2008) Adaptive
data hiding in edge areas of images with spatial LSB domain
systems. IEEE Trans Inf Forensic Secure 3(3):488–497, 45
ISSN: 2231-5381
Mr. T V S Gowtham Prasad
Assistant
Professor,
Dept
of
ECE,
SreeVidyanikethan Engineering College, A.
Rangampet, Tirupati received B.Tech in
Electronics and Communication Engineering from
SVEC, A .Rangampet, Tirupati and M.Tech
received from S V University college of
Engineering, Tirupati. Pursuing Ph.D from JNTU,
Anantapur in the field of Image Processing as ECE
faculty. Interesting Areas are Digital Signal
Precessing, Array Signal Processing, Image
Processing, Video surveillance, Embedded
Systems, Digital Communications
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