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
DOI : 10.5121/ijist.2012.2408 83
Colour Image Steganography Based on Pixel Value
Differencing in Spatial Domain
J. K. Mandal and Debashis Das
Department of Computer Science and Engineering,
University of Kalyani, Kalyani,
West Bengal, India
E-mail: jkm.cse@gmail.com , debashisitnsec@gmail.com
ABSTRACT
In a color image every pixel value composed of red, green and blue component and each of which ranges
from 0 to 255 in case of 8-bit representation. In this paper, we have used pixel value differencing (PVD)
method for secret data embedding in each of the component of a pixel in a color image. But when we use
pixel-value differencing (PVD) method as image steganographic scheme, the pixel values in the stego-
image may exceed the range 0~255. We have eliminated this overflow problem of each component pixel.
Furthermore for providing more security, we have used different number of bits in different pixel
components. It would be very difficult to trace how many bits are embedded in a pixel of the stego image.
From the experiments it is seen that the results obtained in proposed method provides better visual quality
of stego-image compared to the PVD method.
Keywords
Steganography, Pixel-value differencing, Pixel component, Stego-image
1. INTRODUCTION
Security measures have become very necessary issue in the age of digital transmission of
information via Internet. Two schemes are used to protect secret messages from being captured
during transmission. One is encryption where the secret information is encoded in another form
by using a secret key before sending, which can only be decoded with secret keys. The most
popular encryption techniques are DES, RSA etc. Other way is steganography which is a
technique of hiding secret information into a cover media or carrier. If the cover media is a digital
image, it is called cover image and the cover image with hidden data is called stego-image.
Steganographic technique can be used in military, commercial, anti-criminal and so on. There are
various steganographic techniques available where a digital image is used as a carrier. The most
common and simplest method is least-significant-bit (LSB) substitution, where the LSB position
of each pixel of the cover image is replaced by one bit of secret data. Wang et al.[7] proposed a
method to embed data by using genetic algorithm to improve the quality of the stego-image.
However, genetic algorithm takes more computational time. Chang et al. Proposed[9] an efficient
dynamic programming strategy to reduce the computational time. Chan and Cheng [11] proposed
to embed data by simple LSB substitution with an optimal pixel adjustment process. Wu and
Tsai[1] proposed a new scheme to hide more data with outstanding quality of stego-image pixel-

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value-differencing (PVD) method. Thereafter, based on PVD method various approaches have
been proposed [3,4,5,8,10]. In this paper, a steganographic approach on color images, using PVD
has been proposed. The colour pixel-components may exceed the range 0~255 in the stego image
when applying PVD method. We also have removed this drawback of PVD method here. In the
proposed method a digital colour image has been used as a cover image. It will provide more
security in data hiding and also better stego image quality than Wu-Tsai’s PVD method.
2. REVIEW OF PVD METHOD
In PVD method[2], gray scale image is used as a cover image with a long bit-stream as the secret
data. At first the cover image is partitioned into non-overlapping blocks of two consecutive
pixels, pi and pi+1. From each block the difference value di is calculated by subtracting pi from
pi+1. The set of all difference values may range from -255 to 255. Therefore, |di| ranges from 0 to
255. The blocks with small difference value locates in smooth area where block with large
difference values are the sharp edged area. According to the properties of human vision, eyes can
tolerate more changes in sharp-edge area than smooth area. So, more data can be embedded into
edge area than smooth areas. Therefore, in PVD method a range table has been designed with n
contiguous ranges Rk (where k=1,2,…,n) where the range is 0 to 255. The lower and the upper
bound are denoted as lk and uk respectively, then Rk ∈[lk,uk]. The width of Rk is calculated as
wk=uk-lk+1.wk decides how many bits can be hidden in a pixel block. For security purpose Rk is
kept as a variable, as a result, original range table is required to extract the embedded data.The
embedding algorithm is given as algorithm 1
Algorithm 1:
1. Calculate the difference value di of two consecutive pixels pi and pi+1 for each block in
the cover image. This is given by di=|pi+1-pi |.
2. Compute the optimal range where the difference lies in the range table by using di. This
is calculated as Ri =min(uk- di ), where uk ≥di for all 1≤ k ≤ n
3. Compute the number of bits ‘t’ to be hidden in a pixel block can be defined as t=⎿log2
wi⏌. where wi is the width of the range in which the pixel difference di is belonging
4. Read t bits from binary secret data and convert it into its corresponding decimal value b.
For instance if t=010, then b=2
5. Calculate the new difference value diʹ which is given by diʹ=l i +b
6. Modify the values of pi and p i+1 by the following formula:
(piʹ, p i+1ʹ) = (pi+⎾m/2⏋,pi+1-⎿m/2⏌), if pi≥p i+1 and di'>di
(pi-⎿m/2⏌,pi+1+⎾m/2⏋), if pi<pi+1 and diʹ>di
(pi-⎾m/2⏋),p i+1+⎿m/2⏌), if pi≥p i+1 and d'i≤di
(pi+⎾m/2⏋,p i+1-⎿m/2⏌), if pi<p i+1 and di'≤di
where m=|di'-di|. Now we obtain the pixel pair (pi',pi+1') after embedding the secret data into pixel
pair (pi,pi+1). Repeat step 1-6 until all secret data are embedded into the cover image. Hence we
get the stego-image.
When extracting the hidden information from the stego-image, original range table is required. At
first partition the stego-image into pixel blocks , containing two consecutive non-overlapping
pixels each. Calculate the difference value for each block as di'=|pi'-p i+1'|.Then find the optimum
range Ri of d'i . Then b' is obtained by subtracting li from di'. Convert b' into its corresponding
binary of ‘t’ bits, where t=⎿log2 wi ⏌. These t bits are the hidden secret data obtained from the
pixel block(pi',pi+1').

85
3. PROPOSED METHOD
Every pixel in a colour image composed of three colours (channels) i.e. Red, Green and Blue. So,
every pixel contains 24 bits (for 8-bit representation) where 8 bits for red component, 8 bits for
green and 8 bits for blue component in a pixel, shown in Fig.1. In the proposed technique, all the
three components have been used for data embedding. First, we have separated each colour
component from a pixel and get three separate M*N matrix, one for each component colour,
where the original image size is M*N. Now, apply pixel value differencing method for data
hiding in each matrix separately, but in a sequencing manner. First embed bits in 1st
pixel block
(two consecutive non-overlapping pixels) of the red component matrix, then in 1st
block of green
component matrix and lastly in blue component matrix, then again 2nd
block of red matrix and so
on. In this way secret data is embedded into the total image. Further we have embedded different
number of bits for different component pixel blocks for increasing security as well as improving
the visual quality of the stego-image, shown in Fig.2.
Fig.1 Components of a colour pixel
Difference Range Table
G(max 3bits)
R(max 5 bits)
B(max 7 bits)
Fig.2 Number of bits can be hidden in various components
In addition, it is seen that pixel values in the stego image may exceed the range 0~255 on
applying PVD which is not desirable as it may lead to improper visualisation of the stego image.
In this section we introduce a technique to overcome this problem. In the proposed method we
have used the original PVD method to embed secret data. If any pixel value exceeds the range (0
to 255), then check the bit-stream ‘t’ to be hidden. If MSB(most significant bit) of the selected
bit stream ’t’ is 1 then we embed one less number of bits, where MSB position is discarded from
t; otherwise the bit number of hidden data depends on wi . For instance, if pixel value exceeds the
range and selected bit-stream t=101, then set t=01 and embed it. If it is seen that the pixel value
again exceeding range, then embed the value at one pixel, rather than both pixels(of the pixel
block), which will not exceed the range after embedding; where the other pixel is kept
unchanged. It will keep the pixel values within the range because both pixels of a block cannot
exceed at the same time as per the PVD method by Wu and Tsai. Keep the information within
each block, whether one less bit is embedded or not, as overhead. The embedding algorithm is
presented in section 3.1. Fig.3 shows the block diagram of the embedding algorithm.
3.1. Embedding Algorithm
Step 1: Separate the colour image into three component colour matrix and apply the following
steps on each of them sequentially i.e. apply following steps on 1st
pixel block of RED
Three components of a colour pixel One true colour pixel
RED(226) GREEN(137) BLUE(125)
(11100010) (10001001) (01111101)
(226 , 137 , 125)
(11100010 , 10001001 , 01111101)
0~7 8~15 16~31 32~63 64~127 128~255

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matrix, then 1st
block of GREEN matrix and finally on BLUE matrix, then started from
2nd
block of each matrix and so on.
Step 2: Calculate the difference value di for each block of two consecutive non-overlapping
pixels pi and pi+1 , is given by di=|pi-pi+1|.
Step 3: Find optimal range Ri for the di such that Ri =min(ui-di) , where ui≥di .Then Ri ∈[li , ui] is
the optimum range where the difference lies.
Step 4: Compute the amount of secret data bits t from the width wi of the optimum range, can
be defined as t=⎿log2 wi⏌.
Step 5: For RED matrix, if t ≤ 5 then execute the next steps; otherwise no embedding in the
block
For GREEN matrix, if t ≤ 3 then execute the next steps; otherwise no embedding
For Blue matrix execute the following steps.
Step 6: Read t bits and convert it into a decimal value b. Then calculate the new difference value
by the formula di'=li+b .
Step 7: Now, calculate the pixel values after embedding t bits (pi' , pi+1')by original PVD method.
Step 8: Check the embedded pixel values whether it exceed the gray-level range or not . If it
exceeds then check the embedded bit-stream t . Otherwise go to step 9.
Step 8.1: If the left most position of the bit-stream is 1 then select ‘t’ by discarding one bit from
its left most position. Convert ‘t’ bits into its corresponding decimal value b and find
new difference value as di'=li+b . Otherwise do not discard any bit from ‘t’ and calculate
di' .
Step 8.2: Calculate new pixel values (pi' , pi+1') using original PVD method and check again if it
is in the gray range. If it is in the range then go to step 9.Otherwise do the following :
(pi' , pi+1')=(pi-m , pi+1), if pi+1 ≥ pi and pi+1 crossing the upper range(i.e 255) ;
(pi ', pi+1')=(pi , pi+1-m), if pi+1<pi and pi crossing the upper range(i.e 255) ;
(pi' , pi+1')=(pi , pi+1+m), if pi+1≥pi and pi crossing the lower range(i.e 0);
(pi ', pi+1')=(pi+m , pi+1), if pi+1<pi and pi+1 crossing the lower range(i.e 0) .
where m=|di'-di| .
Step 9: Now, the pixel block (pi , pi+1) is replaced by (pi' , pi+1').
Step 10: To keep the information whether ‘t’ bits or ‘t-1’ bits has been embedded, do the
following for each modified block :
Step 10.1:
If no bit has been discarded then do the following:
LSB of P'i LSB of P'i+1 then do

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i) 0 0 P'i+1 +1
ii) 0 1
a) P'i+1 <255 and P'I ≥ 0 P'i+1 +1
b) P'i >0 and P'i+1 =255 P'i-2 and P'i+1 -1
c) P'i=0 , P'i+1=255 P'i +1
iii) 1 0 P'i -1
iv) 1 1 P'i -1
Step 10.2:
One bit has been discarded
LSB of P'i LSB of P'i+1 then do
i) 0 0 P'i +1
ii) 0 1 P'i +1
iii) 1 0
a) P'i+1 >0 and P'i ≤ 255 P'i+1 -1
b) P'i <255 and P'i+1 =0 P'i+2 and P'i+1+1
iv) 1 1 P'i+1 -1
Step 11: Now we get the stego blocks and hence the stego image.
3.2. Extraction Algorithm
Figure 3 shows the block diagram of the extraction algorithm. The steps used for extracting the
hidden data are as follows :
Step 1: Divide the stego image into three component matrix RED, GREEN and BLUE and
execute the following steps for each pixel block, consist of two consecutive non-overlapping
pixels, of RED, GREEN, BLUE respectively i.e. extract bits from one stego pixel at a time.
Step 2: Check the LSB positions of Pi (first pixel of the block) for each block and do the
following :
LSB of Pi Convert it as
0 Pi +1
1 Pi -1

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Step 3: Now calculate the difference value di of two consecutive pixels of each block by using the
formula di=|pi-pi+1| .
Step 4: Find the appropriate range Ri for the difference di.
Step 5: Calculate the number of bits hidden into each block by the formula t=⎿log2 wi⏌, wi is
the width of the optimal range Ri .
Step 6: If operating on RED block and ‘t’ ≤ 3 then execute following steps; otherwise no
extraction
If operating on GREEN block and ‘t’ ≤ 5 then execute following steps; otherwise no
extraction
If operating on BLUE block and ‘t’ ≤ 7 then execute the following steps
Step 7: Extract ‘t’ bits, by the extracting method of original PVD.
Step 8: Check the LSB of Pi . If it is 1, replace the MSB position of extracted ‘t’ bits with ‘1’.
Otherwise do nothing.

89
Fig. 3 Block diagram of embedding and extraction algorithm

90
4. EXPERIMENTAL RESULTS
The proposed scheme hides secret data into a colour image using pixel value differencing method
but various bits in different channels or colour components. Also the problem of overshooting
0~255 range for each component pixels, occurs in PVD, has been removed here. This scheme
results better visual quality of the stego image. To prove the proposal practically we have used C
programming language to implement our scheme and Wu-Tsai’s PVD scheme. We have also
shown the management of pixel value range for a gray scale cover image, where the capacity
remains same with original PVD method. The range table width used here are wi ={ 8, 8, 16, 32,
64, 128 }.We have used cover images of size 512*512 and hide a large bit stream as the secret
information. We have used the Peak-Signal-to-Noise ratio (PSNR) to evaluate the quality of
stego-image. The experimental results are shown in Fig. 4 for colour image and Fig. 5 showing
the results of gray images. A table of results for colour images is given in Table 1 depicting the
capacity of hidden message and the PSNR values. The comparison, of message payload and
PSNR value, between proposed method (in both gray image and colour image) and original PVD
method is shown in Table 2. The PSNR value and payload capacity of each stego-image is given
as average value by executing 100 rounds using standard digital images where the hidden
information are different.
5. ANALYSES AND DISCUSSION
From the experimental results, using colour image as cover media, we can see that PSNR values
are increasing for every stego image compared to original PVD method. In a colour image, the
contribution of the component colours (red, green or blue) of a pixel is different. Green
component contributes 59% where red component contributes 30% and blue component provide
11% contribution to make a colour pixel. So, we have embedded more bits to blue channels and
lower bits to the green and red components. This results less distortion of the pixels in stego
image. Also it provides more security because different number of bits has been hidden in
different channels of a pixel, so is hard to trace how many bits are hidden within a pixel. On the
other hand, according to the proposed method, if pixel component value exceeds 0~255 ranges on
embedding, one MSB bit is discarded from the selected bit-stream to be hidden. So, decimal
value of the reduced bits will be half or less than half. As a result the distortion of the pixel value
in the stego-image will be less. Also It should be noted that whether the pixel exceeds range or
not, we have to keep the information in every component pixel block by adding or subtracting 0,
1, or 2. But this has no remarkable effect for reducing PSNR value for colour image.
In case of gray scale cover image, capacity remains same for the proposed method as in original
PVD method. PSNR values have been changing between -0.62 to +0.32 dB. For gray image
pixels, keeping of overhead information affects the PSNR to reduce in some cases.

91
Fig.4. Cover images and their corresponding stego images
Cover image Stego image Cover image Stego image
Fig.5. Cover images and corresponding Stego images for gray scale image
Table 1: Experimental results of the proposed method
Cover image
(512 512
Proposed method
Capacity PSNR
Lena 145787 42.26
Baboon 144916 38.44
Jet 145648 42.60
Pepper 145995 42.28
Girl 144285 42.80
House 145374 41.41
Splash 146732 42.86
Sailboat 143278 40.66
Capacity in Bytes and PSNR in dB and Cover images of size 512*512
Table 2: Comparison of results of the proposed and Wu and Tsai’s method
Cover image
(512 512
Wu and Tsai’s
method
Payload PSNR
Proposed method
Gray Image Colour Image
Payload PSNR Payload PSNR
Lena 1.56 41.70 1.56 40.61 1.48 42.26
Baboon 1.75 36.86 1.75 36.67 1.47 38.44
Jet 1.55 41.31 1.55 40.94 1.48 42.60
Pepper 1.55 40.55 1.55 40.61 1.48 42.28
Payload in bpB and PSNR in dB and Cover images of size 512*512 .

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6. CONCLUSIONS
In this paper, we have discussed a steganographic method for data hiding by using pixel value
differencing in colour images which also guarantees that no pixel value will exceed the range 0 to
255 in stego-image. We have shown the pixel range overflow management also for gray scale
images. We have used original PVD method where pixel value does not cross the range,
elsewhere proposed method has been used for embedding data. Proposed scheme on colour
images gives more security than the original PVD used in grey images and also provides better
visual quality of stego-image. Furthermore, proposed method extracts the hidden secret message
efficiently without using the original cover image.
ACKNOWLEDGEMENTS
The authors express gratitude to the Department of Computer Science and Engineering,
University of Kalyani and the PURSE scheme of DST, Govt. of India, under which the research
has been carried out.
REFERENCES
[1] J.K. Mandal, Debashis Das “Steganography Using Adaptive Pixel Value Differencing(APVD) for
Gray Images through Exclusion of Underflow/Overflow ”, Computer Science Information Series,
ISBN : 978-1-921987-03-8, pp. 93-102, 2012.
[2] H.C. Wu, N.I. Wu, C.S. Tsai and M.S. Hwang, “Image steganographic scheme based on pixel value
differencing and LSB replacement method”, IEEE Proceedings on Vision, Image and Signal
processing, Vol. 152, No. 5,pp. 611-615, 2005.
[3] Schneier B.:'Applied cryptography'(John Wiley Sons, New York, 1996, 2nd
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[4] W. bender, D. gruhl, N. Morimoto, A.Lu, “Techniques for data hiding”, IBM Systems Journal Vol.
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IEEE,VOL. 87,PP. 1062-1078, 1999.
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Authors
Jyotsna Kumar Mandal, M.Tech(Computer Science, University of Calcutta),
Ph.D.(Engg. , Jadavpur University) in the field of Data Compression and Error
Correction Techniques, Professor in Computer Science and Engineering, University of
Kalyani, India. Life member of Computer Society of India since 1992 and life member
of Cryptology Research Society of India. Dean Faculty of Engineering, Technology
and Management, working in the field of Network Security, Steganography, Remote
Sensing GIS Application, Image Processing. 25 years of teaching and research
experiences. Eight scholars awarded Ph.D., one submitted and eight are pursuing. Total
number of publications is more than two hundred and thirty in addition of publication of five books from
LAP, Germany .
Debashis Das pursuing his M. Tech. in Computer Science and Engineering from
University of Kalyani, under the Department of Computer Science and Engineering.
Received his B. Tech degree in Information Technology in 2009. He has two public
ations in the in ternational conference proceedings.

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