The document proposes a new hybrid steganographic method that embeds data in grayscale images while preserving the histogram characteristics. It uses pixel value differencing (PVD) and least significant bit (LSB) substitution. The method divides the pixel range into lower and higher levels and embeds more bits in higher levels. Each 3x3 block has a base pixel where 3 bits are embedded using LSB replacement. Remaining pixels are embedded using PVD. Experiments show it provides higher embedding capacity and image quality than existing methods while introducing fewer changes to the histogram.

1. A new hybrid steganographic method for histogram
preservation
Umesh Ghanekar
Abstract— This paper presents a histogram preserving data
embedding method for grey-scale images which is based on pixel
value differencing (PVD) and least-significant-bit (LSB)
substitution methods. Various PVD based steganographic
methods achieve high data embedding capacity with minimum
distortions in stego image at the cost of change in histogram
characteristics which is can be detected by histogram based
steganalysers. This persistent problem can been taken care off by
proposed method of data hiding. The improved performance of
the proposed method is verified through extensive simulations.
Keywords—steganography; PVD; embedding capacity;
histogram characteristics;
I. INTRODUCTION
In recent years, steganography has emerged as an
interesting area of research. Steganography is basically used to
enhance the communications security. It hides the very
existence of the secret message into the cover media such as
digital image, audio, video, text etc [1]. In this paper, grey-
scale digital images have been used as the cover media for
hiding the secret message.
Many data hiding methods have been proposed so far and
among them the most simple and well- known steganography
method is least-significant-bit (LSB) replacement. Here, the
secret message is concealed directly into the LSBs of each
pixel of an image. This direct embedding procedure of various
existing spatial domain steganographic methods like LSB
replacement and others is incapable of exploiting the true
embedding capacity of any cover image. An image consists of
two areas i.e. edge area and smooth area. Edge areas can be
embedded with more number of bits than smooth areas, as
edges are less sensitive towards the changes in pixel
intensities. In 2003, Wu and Tsai used this concept and
presented a steganography method using PVD [2]. This
method hides different amount of secret bits in consecutive
non-overlapping pixel pairs by taking the difference value
between the pixels of a pixel pair. Further to increase the
embedding capacity a hybrid method based on PVD and fixed
sized LSB method was proposed by Wu et al. [3]. In 2008,
another hybrid method was presented based on PVD and
modulus function [4]. This method provides higher
imperceptibility of the stego image than the previous methods
while maintaining good data embedding capacity. An adaptive
LSB replacement method was also proposed in 2008 which
utilises the basic concept of data hiding based on human visual
system (HVS) [5]. As a result, pixels are embedded with
different number of secret bits using LSB replacement
method. In the year 2012, a novel adaptive data hiding method
based on LSB substitution and PVD was proposed [6]. This
method is able to conceal large amount of secret data and
provide good stego image quality but is unable to preserve
histogram characteristics.
Here, we have proposed a steganographic method using
LSB substitution and PVD in order to preserve the image
histogram. In this method, we have increased the block size to
3 3 as compared to 1 3 of adaptive LSB and PVD [6]. The
central pixel of each block is termed as base pixel and 3-bits
are embedded in this pixel with the help of LSB replacement
method and optimal pixel adjustment process (OPAP) [7].
Remaining pixels of the block are embedded with secret data
bits using PVD. The performance of the proposed method is
demonstrated through extensive simulations.
The paper is organized as follows. Section II presents the
proposed method. Experimental results are shown in section
III. Finally, conclusions are given in section IV.
II. PROPOSED METHOD
This section deals with the procedure of proposed method
which consists of three phases, namely, the range division
phase, the embedding phase and the extracting phase. These
phases are described as follows.
A. Range division phase
Prior to embedding the secret message, the grey level range
[0,255] is divided into five ranges where
, denotes the lower bound of the range
and denotes the upper bound of the range . These five
ranges can be , , ,
and . Fig. 1 shows the dividing
case i.e. div=31 for the proposed method. It divides the range
[0,255] into „lower level‟ which consist of ranges
, , and „higher level‟ which
include ranges , . Let are the
number of bits to be embedded in the pixels falling under the
range . According to HVS, changes in edge areas are less
visible than smooth areas and hence more data can be
Priya darshni,
Dept. of Electronics and Communication Engineering National Institute of Technology
Kurukshetra, India
priyadarshni.ece@gmail.com, ugnitk@nitkkr.ac.in
Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
139 NITTTR, Chandigarh EDIT-2015

2. embedded in edges. In the proposed method, first three ranges
( fall under the category of smooth regions
whereas last two ranges falls in the edge regions.
Therefore, we propose to embed bits in the lower level and
bits in the higher level.
B. Embedding phase
The cover image is divided into consecutive non-
overlapping blocks of size 3 3 in raster scan manner. Each
block has a centre pixel named as base pixel . Data
embedding in each block is performed by the following steps
as given in [6].
Step 1: Consider 3-rightmost LSBs of and transform these
three LSBs to a decimal value, say . Read 3-bits from
binary secret data in continuation and replace the 3 LSBs of
with these binary secret data bits to obtain . Also,
transform these bits to a decimal value, say .
Step 2: Compute the difference value using .
Step 3: Modify using OPAP as follows
{
(1)
Step 4: Compute the absolute difference values between the
base pixel and other pixels of the block by using
| | (2)
where and denotes the location of the pixel in a block.
Therefore, eight difference values are calculated.
Step 5: Assign the ranges corresponding to the differences
found in Step 4 and obtain the lower bounds too i.e. .
Accordingly, calculate which denotes the number of bits to
be concealed into eight pixels.
Step 6: Read bits in continuation from the binary secret
message and transform these bit-sequences into decimal
values, say . Now, compute the new difference values
using
(3)
Step 7: Calculate the two new values of each pixel of a block
using
(4)
Step 8: Choose the best new value for these pixels from the
values obtained in Step 7 using
{
| | | |
(5)
Repeat the above procedure for every block of the cover
image so as to obtain the final stego image.
C. Extracting phase
At first, the stego image is divided into consecutive non
overlapping blocks of size 3 3 and then for the complete
extraction of the secret message following steps are executed.
Step 1: Select the centre pixel as the base pixel and extract 3-
LSB bits from it. Call this binary sequence as .
Step 2: Calculate the absolute difference values between
the base pixel and the other pixels of a block and then find the
range to which these difference values belong to. Then, obtain
the lower bound of the corresponding range and also
determine the number of bits to be extracted from each
pixel.
Step 3: Obtain the secret data sub-streams as by taking the
difference between above calculated difference values and
respective lower bounds. Transform to binary strings with
length equivalent to .
Finally, concatenate , to obtain the original bit sequence
of the secret message.
Lower-level Higher-level
𝐼𝑅 =[0,7] 𝐼𝑅 =[8,15] 𝐼𝑅 =[16,31] 𝐼𝑅 =[32,63] 𝐼𝑅 =[64,255]
Fig.1 The dividing case (div=31) of the proposed method with „lower level‟ and higher level‟.
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3. III. EXPERIMENTAL RESULTS
The simulation is done using several 8-bit grey-scale
images of size 512 512 taken from SIPI image database [8].
The secret message to be embedded is in the form of text.
The objective criterion used for evaluating the distortions
in the stego image is PSNR and is given by:
1 1
1
( ( , ) ( , ))
M N
i j
MSE C i j S i j
M N  
 


2
1010log
Max
PSNR
MSE
 
  
 
(6)
where and denotes the image size, and
represents the corresponding cover and stego image pixels. A
high PSNR value denotes that there is less dissimilarity
between cover and stego image. It can be seen from Table I
that the proposed method provides improved embedding
capacity and stego image quality within acceptable limits. For
subjective evaluation images are shown in Fig. 2 which shows
that the changes are unobservable even after large amount of
data hiding.
Besides providing large data hiding capacity and good
quality of stego image, a steganographic method needs to be
resistant against steganalytic attacks such as chi-square [9], r-s
steganalysis [10], HCF COM [11] and others. Mainly the
steganalysers are based on image histogram, therefore, if the
histogram characteristics are preserved properly then high
resistance against the well-known detectors can be achieved.
The criteria for evaluating the changes in image histogram is
to compute the number of uncompensated changes after full
data embedding and is given by [12]:
255
0
( ) ( )
2
c s
i
h i h i
uc 



(7)
(a) (b)
Fig. 2 (a) Test cover images and (b) stego images obtained using our proposed
method.
Cover Adaptive LSB–PVD method Proposed method
images
Capacity, bit Bit rate, bpp PSNR, dB Capacity, bit Bit rate, bpp PSNR, dB
Boat 789307 3.01 34.8337 793034 3.02 35.2782
Barbara 806597 3.07 32.9321 809647 3.08 32.9764
Couple 785793 2.99 32.6727 790605 3.01 32.7946
Man 792879 3.02 33.6723 795835 3.03 33.3933
TABLE I. COMPARSIONS OF THE RESULTS BETWEEN ADAPTIVE LSB SUBSTITUTION-PVD METHOD (TYPE 1 DIVISION (K=3)) AND
THE PROPOSED METHOD
Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
141 NITTTR, Chandigarh EDIT-2015

4. where, stands for uncompensated changes while and
represents the histogram of cover and stego images. Fig. 3
shows that the proposed method performs better in
preserving the histogram as the average number of
uncompensated changes of the proposed method are less i.e.
11877 as compared to 15668 of adaptive LSB-PVD method
[6].
Fig. 3 Uncompensated changes in histogram after embedding via our method
and adaptive LSB subs.-PVD method.
IV. CONCLUSIONS
In this paper, we have presented a histogram preserving
data hiding method which is based on LSB substitution and
PVD. This method can hide large amount of secret data as
well as provide an imperceptible stego image quality while
compensating for the dissimilarity between the histograms of
the cover and stego images. This advantage of keeping the
change in image histogram within permissible limit helps the
proposed method to show better resistance against histogram
based steganalysers. The efficacy of the proposed method is
verified via several experimental results which yielded better
performance in comparison with adaptive LSB -PVD method.
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