Journal of Signal and Information Processing, 2013, 4, 343-350
Published Online November 2013 (http://www.scirp.org/journal/jsip)
http://dx.doi.org/10.4236/jsip.2013.44043
Open Access JSIP
343
Implementation of Variable Tone Variable Bits Gray-Scale
Image Stegnography Using Discrete Cosine Transform
Sahib Khan1*, Muhammad Nawaz Khan1, Somia Iqbal1, Syed Yaqoob Shah2, Nasir Ahmad2
1Department of Electrical Engineering, University of Engineering & Technology, Peshawar, Pakistan; 2Department of Computer Sys-
tem Engineering, University of Engineering & Technology, Peshawar, Pakistan.
Email: *engrsahib_khn@yahoo.com
Received July 23rd, 2013; revised August 24th, 2013; accepted September 1st, 2013
Copyright © 2013 Sahib Khan et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
Secure exchange of information is the basic need of modern digital world of e-communication which is achieved either
by encrypting information or by hiding information in other information called cover media. Concealing information re-
quires a well designed technique of Stegnography. This work presents a technique, variable tone variable bits (VTVB)
Stegnography, to hide information in a cover image. The VTVB Stegnography hides variable data in discrete cosine
transform (DCT) coefficients of the cover image. VTVB Stegnography provides variable data hiding capacity an d vari-
able distortion. Additional large data hiding this technique provide extra security due to the large key size making
VTVB Stegnography technique much more immune to steganalysis. The hiding makes the existence of information
imperceptible for steganalysis and the key of keeping a secret makes the recovering of information difficult for an in-
truder. The key size is depending on cover image and numbers of bits of discrete cosine transform (DCT) coefficients
used for information embedding. This is a very flexible technique and can be used for low payload applications, e.g.
watermarking to high payload applications, e.g. network Stegnography.
Keywords: Information Security; Image Processing; Stegnography; Steganalysis; Discrete Cosine Transform (DCT)
1. Introduction
This Stegnography is a method of secure exchange of
information; implemented by concealing covert messages
in cover-medium like text, digital images [1], audios [2,3]
and videos [4]. Stegnography keeps the presence of se-
cret information undetectable and Stego-file having se-
cret information looks like the cover-file. But there are
some chances of detection. To hide information in an
undetectable manner is key feature of a good Steg-
nographic method [4]. A method using a cover medium
with a large degree of redundancy is considered the most
suitable one [5]. The redundant bits are replaced with
information resulting triv ial change in Stego-Image [4,6].
Crandall for the first time presented a matrix coding
technique with improved hiding efficiency [7]. The rela-
tion between Stegnographic codes (Stego-codes) and
covering codes was studied in [8].
Information can be hidden in the cover file in spatial
domain modifying cover elements and transform domain
modifying transform coefficients. This paper presents a
Stegnographic technique used for DCT coefficients of a
cover image. The proposed technique provides a self
encryption and hides variable amount of data in different
DCT coefficients. As each coefficient represents a fre-
quency component (Tone) and different amounts of data
are hidden in different coefficients, that’s why it is name d
as variable tone variable bits (VTVB) Stegnography.
2. Previous Work
Stegnography like Cryptography is technique for secure
communication of information. Various researchers made
their efforts and proposed some best technique of that
time. A brief description of the developments made in
the field of Stegnog raphy is given as:
The mathematical equations of Discrete Cosine Trans-
form (DCT) and its uses in image compression [9] and
the conversion of a signal to its basic components [10]
opened new way for the Stegnography using DCT. A
trustworthy and precise procedure has been proposed by
Jessica Fridrich et al. for detecting least significant bit
*Corresponding a uthor.
Implementation of Variable Tone Variable Bits Gray-Scale Image Stegnography Using Discrete Cosine Transform
344
(LSB) non sequential embedding in digital images [11-
13]. The image signature concept has been implemented
by Mohesen A shourian, R. C. Jain and Yo-Sung Ho [14].
J. R. Krenn has proposed a pseudo-code algorithm to
hide message in LSB of DC coefficients of cover image
[15]. Ren-Junn Hwang et al. have proposed data hiding
based on JPEG technique [16]. H. W. Tseng and C. C.
Chang have proposed a novel high capacity data hiding
method based on JPEG [17]. Youngran Park et al. have
proposed and implemented a method, in spatial domain,
to authenticate whether the hidden message had been
deleted, forged or changed by attackers [18]. Neeta
Deshpandeet et al. have set in data in least significant
bits of cover image [19]. M. Chaumont and W. Puech
have proposed a method with secret key to hide the color
information in a compressed grey-level image [20]. Aneesh
Jain and In-dranil Sengupta have proposed a method,
resistant to JPEG compression, of hiding information
using bitmap image as cover [21]. KokSheik Wong,
Xiaojun Qi, and Kiyoshi Tanaka have proposed Mod4
Stegnography method, capabale of hiding information
into both uncompressed and JPEG compressed image, in
discrete cosine transform (DCT) domain [22]. Takayuki
Ishida et al. have discussed an improved version of
JPEG2000 Stegnography, named QIM-JPEG2000 Steg-
nography, using quantization index modulation (QIM)
[23]. In 2010 Ching-Tsorng Tsai et al. presented a steg-
anographic scheme that conceals secret information in
image mosaics based on tile [24]. In 2011 Chung-Ming
Wang and Peng-Cheng Wang schemes presented two
new scheme SSA and ESA, for digital Stegnography of
point sampled geometry in the spatial domain image fea-
tures [25]. Debnath Bhattacharyya proposed a data hid-
ing technique that exploits some features of audio signals
that was able to hide data from perception robustly [26].
A distance based algorithm named decreasing distance
decreasing bits (DDDB) was proposed and implemented
by Sahib Khan et al. in 2011 [27] and in 2013 modular
distance technique (MDT) was adopted for the imple-
mentation of image Stegnography [28].
3. Proposed Work
Stegnography can be implemented in spatial domain as
well as transform domain. In spatial domain data is hid-
den directly in cover file pixels by varying the signal
intensity while in transfo rm domain the transform coeffi-
cients are modified according the message. This work
deals with the implementation of Image Stegnogr aphy in
Discrete Cosine Transform (DCT). As most of the Signal
energy lies at low frequency in image; it appears in the
upper left corner of the DCT [29]. Due to this distribu-
tion of ener gy the higher fr equency were mostly used for
data hiding. Almost all the previous work used either
fixed data hiding or targeted a specific region of DCT
coefficients for data hiding. This work is presenting a
new technique for data hiding i.e. Stegnography by hid-
ing varying amount of data in different DCT coefficients.
Different amount of data is hidden within different fre-
quency components i.e. DCT coefficients. As in commu-
nication a frequency is also called tone that’s why tech-
nique is termed as Varying Tone Varying Bits Stegno-
graphy.
In varying tone v arying bits (VTV B) Stegnography in -
stead of fixed; varying data is hidden in various DCT
coefficients. As each DCT coefficient’s value is repre-
sented by 16 bits i.e. of type double and any/any number
of the bits can be used for data hiding using VTVB
Stegnography. The number of bits utilized is determined
by the user depending on the requirement i.e. Hiding
Capacity, Signal to Noise Ratio (SNR), Peak Signal to
Noise Ratio (PSNR) and Mean Square Error (MSE)
[30,31]. Using more bits per coefficient for data hiding in
cover image, results in increase in data hiding capacity
and MSE. The DCT coefficients are subject to varying
bits substitution ranging from 0 bits i.e. no hiding to 16
bits. How much number of bits are hidden in which coef-
ficient is the key of VTVB Stegnography; making it dis-
tinctive and more secure from other Stegnography tech-
niques.
In VTVB Stegnography DCT coefficients are arran-
ged in a group of “I” coefficients. The group size de-
pends on the number of bits variation i.e. how many
different no. of bits are used for data hiding in a group.
As in double format each DCT coefficient is represented
using 16 bits so using double format there are 17 dif-
ferent possible combination of no. of bit and any of the
combination/s can be used for data hiding. For example
if a group size of 8 coefficients is used for data hiding
then there are 8 different no. of bits can be used for data
hiding defi ned by the user as gi ven in Figure 1.
In coefficient “C1” of defined group only 1 bit data is
hidden, in coefficient “C2 - C8” of the same group are
subjected to 2, 8, 4, 9, 6, 7 and 8 bits substitution
respectively. The same sequence is followed for the other
groups of the same cover file. The group size may be
varied d epending on application. An y group size of DCT
coefficients, 17 at maximum, can be used. As shown in
Figure 2. The process is repeated for the whole cover
file.
After hiding information in DCT coefficients the In-
verse DCT transform is applied on the modified coeffi-
C1 C2 C3 C4 C5 C6 C7 C8 ………
1 2 8 4 9 6 7 3 ……….
Figure 1. Coefficients and no. of bits assignment for group
size 8.
Open Access JSIP
Implementation of Variable Tone Variable Bits Gray-Scale Image Stegnography Using Discrete Cosine Transfor m 345
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11 C12 C13 C14 C15C16C17
1 2 3 4 5 6 7 8 4 3 6 10 11 … ………
Figure 2. Coefficients and no. of bits assignment for group
size 13.
coefficients to get Stego file.
3.1. Hiding Capacity Stegnography
In VTVB Stegnography variable amount of data is hid-
den in different coefficients of each groups of DCT
coefficient of predefined size. The data hiding capacity
depends totally on the no. of bits assigned to each
coefficient of the group. More bits used for data hiding
greater will be the data hiding capacity. Let consider a
cover grayscale image of size N × M is transformed us-
ing discrete cosine transform (DCT) to get DCT coeffi-
cients and DCT coefficients are divided in to “j” number
of groups with group size “ni.e. each group consists of
n” coefficients and let “Bi” number of bits are hidden in
ith coefficient of jth group. Then the total data hidden in
each group “Dj” will be:
1
n
ji
D
Bi (1)
where Dj is total amount of data hidden in the jth group
of DCT coefficients.
Now the total amount of data hidden in the cover
“Data” will be:
1
Data NM
j
j
D
(2)
The data hiding capacity in bits per pixel (BPP) is of
VTVB Stegnography is:

Data
Capacity BPPNM
(3)
The data capacity in percentage will be:

Data
Capacity %100
8NM

 (4)
The data hiding capacity of VTVB can be varied by
varying the no. of bits to be embedded in DCT co-
efficient/s of the predefined group.
3.2. Key Size of VTVB Stegnography
VTVB Stegnography is a secure technique for data hid-
ing in a cover file. As the data hidden in a coefficient
vary from coefficient to coefficient according to a prede-
fine key. How much number of bits are hidden in which
coefficient is the key of VTVB Stegnography; making it
distinctive and more secure from other Stegnography
techniques.
Consider in image cover image of size N × M.
Applying DCT on the cover image N × M no. of DCT
coefficients are obtained. As each DCT coefficient is
represented by 16 bits and any combination of bits, 0 to
16 bits, can be hidden in a coefficient. So the total
possible combination for a single coefficient “kc” is given
as:
16 16 16 1616
01 231c
kccccc
6
 (5)
16 16
0
c
n
kc
n
(6)
As the key there are a total of N × M no. of
coefficients then the maximum key size “K” is:

16 16
0n
n
K
NM c
 (7)
where K is the maximum key size.
3.3. SNR, MSE and PSNR
SNR, PSNR and MSE is measurement parameters these
parameter used to measure the quality and error between
cover image and Stego image these parameter are calcu-
lated using the following formulas [Gonzalez, 2ed]:

 
2
11
10 2
11
Cov ,
SNR10 logCov ,Stego,
RC
ij
RC
ij
ij
ij Ij






 (8)
 
2
11
1
MSECov ,Stego ,
RC
ij
ij ij
RC

 (9)
2
10 255
PSNR10 log
SE



(10)
4. Implementation of VTVB Stegnography
To hide variable data in discrete cosine transform (DCT)
coefficients using VTVB mechanism, different combina-
tion of least significant bits of each combination are util-
ized. To Implement VTVB Stegnography discrete cosine
transform is applied on cover image resulting in DCT
coefficients. The DCT coefficients are arranged in groups
of specific size varying from 1 to 16. Then each coeffi-
cient of the group is subject to a fix number of bits sub-
stitution for hiding data. The group size and number of
bits substituted in a coefficient are the most important
factor of VTVB Stegnography. These two factors decide
the hiding capacity and the key size. In other words the
security strength of the VTVB imple mented. After hiding
data/information each DCT coefficient the inverse DCT
is applied on the modified coefficients having hidden
data resulting in Stego Image. The whole process is
shown in Figure 3 in detail.
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Implementation of Variable Tone Variable Bits Gray-Scale Image Stegnography Using Discrete Cosine Transform
Open Access JSIP
346
Figure 3. Block diagram of VTVB.
VTVB Stegnography is very flexible technique of data
hiding providing the liberty to use any group size and
any number of least significant bits of DCT coefficient.
In this paper VTVB is implemented different group size
i.e. 1 - 3 and so on and in all groups 1 bit is hidden in 1st
coefficient, 2 bits in 2nd, and 3 bits in 3rd and so on. The
group size and number of bits substituted in each
coefficient of each group are shown in Figure 4 in detail.
For each group size signal to noise ratio (SNR), peak
signal to noise ratio (PSNR), mean square error (MSE)
and hiding capacity is find out and is given in the results
section.
The SNR, PSNR, MSE, hiding capacity and Stego
images for each group size are given in results section.
Hiding one bit in 1st coefficient, two bits in 2nd coeffi-
cient, three bits in 3rd coefficient and so on in each gro up
is not the only way to hide data different number bits
may be used in a coefficient of a group for example we
may hide two bits in 1st coefficient, eight bits in 2nd
coefficient etc as shown in Figure 5.
5. Results
Technology Both qualitative and quantitative analysis is
made for different group of DCT coefficients of different
sizes i.e. 1 - 16 and for each group different number of
bits are substituted in different coefficients. The MSE,
SNR, PSNR and hiding capacity are calculated experi-
mentally by using the combinations of different bits for
different coefficients of different sizes shown in Figures
6(a)-(p). The cover image used for data hiding is shown
in Figure 6(a) and Stego images obtained for group size
1 to 8 are shown in Figures 6(b)-(i) and for rest of the
group size the Stego images are not shown due to sig-
nificant distortion and reduction in contrast level. The
MSE, SNR, PSNR and hiding capacity for each group
size are listed in Table 1 and are shown graphically in
Figures 7-10 respectively.
The experimental results obtained, by implementing
variable tone variable bits (VTVB) Stegnography for
different groups of different sizes, show the behavior of
payload i.e. hiding capacity and quality measuring pa-
rameters i.e. MSE, SNR and PSNR. The results shows that
as the group size increases and more data is hidden in
cover file t he hiding ca paci t y i ncreases graduall y as given
in Figure 7. An average incre ase of 6 percent occurs with
increase of 1 in group size. The results also show the non-
Implementation of Variable Tone Variable Bits Gray-Scale Image Stegnography Using Discrete Cosine Transfor m 347
(
c
)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(d)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(e)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(f)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(g)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(h)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(i)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(j)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(k)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(l)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(m)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(o)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(p)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10C11C12C13C14C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(n)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(a)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
(b)
Figure 4. Various group size and bits substation in the coefficients of each group. (a) Group size 1; (b) Group size 2; (c)
Group size 3; (d) Group size 4; (e) Group size 5; (f) Group size 6; (g) Group size7; (h) Group size 8; (i) Group size 9; (j)
roup size 10; (k) Group size 11; (l) Group size 12; (m) Group size 13; (n) Group size 14; (o) Group size 15; (p) Group size 16. G
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Implementation of Variable Tone Variable Bits Gray-Scale Image Stegnography Using Discrete Cosine Transform
348
linear increasin g trend of MSE with the increase in group
size as shown in Figure 9 while the SNR and PSNR de-
creases with the increasing group size as shown in Fig-
ures 8 and 10.
It is clear from the results listed in Table 1 and the re-
sults presented in graph ical form in Figures 7-10 respec-
tively that increasing group size increases hiding capacity
and MSE while decreasing SNR and PSNR.
6. Conclusion
VTVB Stegnography is a secure technique with a large key
size making the existence of information undetectable at a
low payload level and makes the recovering information
difficult for any unauthorized third party due to its own
encryption mechanism. VTVB has been proven to be much
immune to Steganalysis. It is a flexible technique provid-
ing variable hiding capacity, SNR, PSNR and MSE and
C1C2C3C4C5C6C7C8C9 C10 C11 C12 C13 C14 C15C16
2 8 5 6 1 2 6 109 1 2 5 7 9 3 7
Figure 5. Random selection of number bits for data hiding.
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 6. Cover image and Stego images of different group size. (a) Cover image; (b) Stego image of group size 1; (c) Stego
image of group size 2; (d) Stego image of group size 3; (e) Stego image of group size 4; (f) Stego image of group size 5; (g)
Stego image of group size 6; (h) Stego image of group size 7; (i) Stego image of group size 8.
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Implementation of Variable Tone Variable Bits Gray-Scale Image Stegnography Using Discrete Cosine Transfor m 349
Table 1. Hiding capacity, SNR, PSNR and MS E.
SNO Group Size Capacity (%) SNR (db) PSNR (db) MSE
1 1 12.5000 29.0102 53.0757 0.3203
2 2 18.7500 25.0695 49.1350 0.7936
3 3 24.9512 20.5156 44.5811 2.2645
4 4 31.2500 16.5834 40.6489 5.6000
5 5 37.4023 11.1257 35.1912 19.6771
6 6 43.5547 7.0410 31.1065 50.3997
7 7 49.7070 4.8326 28.8982 83.8036
8 8 56.2500 4.8056 28.8711 84.3276
9 9 62.0117 4.0229 28.0884 100.9801
10 10 68.1641 3.5679 27.6334 112.1351
11 11 74.4141 3.2933 27.3588 119.4542
12 12 80.4688 3.1353 27.2008 123.8784
13 13 86.6211 3.0820 27.1475 125.4099
14 14 92.7734 3.0287 27.0942 126.9580
15 15 99.6582 3.0192 27.0847 127.2347
16 16 106.2500 3.0048 27.0490 128.2874
Figure 7. Capacity of VTVB from 1 to 16 bits.
Figure 8. SNR of VTVB from 1 to 16 bits.
Figure 9. MSE of VTVB from 1 to 16 bits.
Figure 10. PSNR of VTVB from 1 to 16 bits.
can be used for all types of applications requiring low
hiding or large hiding capacity. SNR and PSNR de-
crease with an increase in hiding capacity and MSE trade
is made between these parameters depending on applica-
tion.
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