 Open Access
 Total Downloads : 289
 Authors : Nitheesh C. N. , Arathy C. Haran V.
 Paper ID : IJERTV4IS061111
 Volume & Issue : Volume 04, Issue 06 (June 2015)
 DOI : http://dx.doi.org/10.17577/IJERTV4IS061111
 Published (First Online): 01072015
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Reversible Data Hiding Method Based on Two Dimensional Difference Histogram and Block Division
Nitheesh C. N.
M. Tech Student
Dept. of Electronics and Communication Engineering
MBCET, Nalanchira Kerala, India
Arathy C. Haran V.
Assistant Professor:
Dept. of Electronics and Communication Engineering MBCET, Nalanchira
Kerala, India
Abstract Reversible data hiding (RDH) aims to embed secret message into a cover image by slightly modifying its pixel values, and the embedded message as well as the cover image can be completely recovered from the marked content. Many RDH methods have been proposed e.g., the methods based on lossless compression, difference expansion, histogram modification, predictionerror expansion, and integer transform etc. The histogrambased methods modify the histogram in such a way that certain bins are shifted to create vacant space while some other bins are utilized to carry data by filling the vacant space. This type of methods can control the embedding distortion and provide a sufficient embedding capacity. The existing technique is based on twodimensional difference histogram modification. Here reversible data hiding (RDH) scheme is done by using differencepairmapping (DPM). First, by considering each pixel pair and its context, a sequence consisting of pairs of difference values is computed. Then, a two dimensional difference histogram is generated by counting the frequency of the resulting differencepairs. Reversible data embedding is implemented according to a specifically designed DPM. Here, the DPM is an injective mapping defined on differencepairs. The embedding capacity of the existing data hiding technique can be increased by combining block division of the input image and the 2D Difference histogram modification. The block division is done to improve the data hiding capacity, the marked image quality and to improve the hiding places of information in the image.
Keywords Embedding capacity, RDH, DPM, twodimensional difference histogram.

INTRODUCTION
Large amount of data in digital form is transferred through internet. Data hiding is necessary to improve security. Data hiding is a form of steganography that embed a secret message into the cover image. The data to be embedded is referred to as the payload and the embedded image is known as stego image. The main requirement of data hiding is that the hidden information should be invisible and also the marked image quality should be high. Many data hiding techniques have been proposed so far. Among them reversible data hiding has attracted the attention. The term reversible in reversible data hiding
referred to as lossless, i.e. both the embedded data and the cover image can be successfully decoded.
The histogram based method attained attention among the reversible data hiding scheme [4]. Ni et al. [5] proposed histogram based method. It considers maximum and minimum points of the histogram in data embedding. But this method has the disadvantage of low embedding capacity and it cannot embed data if the histogram is flat. In 2006, Lee et al. [7] proposed the reversible data hiding scheme based on difference histogram. The method uses the pixel pair with difference 1 or 1 to carry the data. In 2009 Tai et al. [8] used binary tree structure (BTS) in difference histogram. The embedding capacity has been improved by Tai et al. [8] method. Hong [9] improved Tai et al.s method by using dual binary tree structure (BTS). The embedding capacity is further improved with distortion at the same level. Then the median edge detector and error energy estimators are used to identify more pixels which can be used for embedding.
In histogram based method the pixel pair of the given image is considered. Then, the difference value of the pixel pair is obtained and it is projected to one dimensional space [3]. The one dimensional histogram is generated by counting the difference value. The histogram bin with high peak is used to embed data and other bins are shifted. In the two dimensional histogram based method, by considering a pixelpair and its context, a local image region is projected to a twodimensional space to obtain a difference pair sequence [1]. A twodimensional differencehistogram is then generated by counting the differencepairs. Finally, reversible data embedding is implemented according to a particularly designed differencepairmapping (DPM) [1]. Here, the DPM is an injective mapping defined on differencepairs and it is a natural extension of expansion embedding and shifting techniques used in current histogrambased methods [1]. By using the twodimensional differencehistogram and this specific DPM compared with the conventional one dimensional histogram based methods more pixels are used for carrying data and thus an improved embedding
performance is achieved [1]. In the proposed method by using block division on the image and applying DPM to each block more data can be embedded.

PROPOSED METHOD
The proposed method based on block division is used to improve the data hiding capacity, the marked image quality and to improve the hiding places of information in the image. In this reversible data hiding technique image is divided into blocks [2] and the data are embedded into each block. The data embedding, takes place in three steps:
(1) Divide the image into blocks (2) Two dimensional histogram generation of each block (3) Data embedding using difference pair mapping. By block division more pixels of each block can be selected suitable for data embedding. And, thus the overall data embedding capacity can be increased. Also, the PSNR value of each block can be modified so that the overall PSNR value can be increased.
The data embedding is based on difference pair mapping (DPM) and the two dimensional difference histogram. The difference pair mapping (DPM) is a natural extension of expansion embedding and shifting algorithm. The embedding process is based on pixel pairs. The cover image is divided into nonoverlapping pixel pairs [1]. One of the pixels in the pixel pair is modified during embedding and the other is shifted. The maximum modification to the pixel will be one so the marked image quality will be high. If the pixel value of the pixel pair is 0 or 255, overflow or underflow will occur and the data embedding is not done.
In difference pair mapping, a pixel pair , is
in the image can be calculated by taking the sum both horizontal and vertical difference of pixels in the block. The pixel block for the GAP predictor is shown in fig. 1.
j j+1 j+2 j+3
x
y
1
2
3
4
5
6
7
8
9
10
i i+1 i+2
Fig. 1. pixel block for GAP predictor
The modification of the difference pair 1 , 2 is in four directions: 1 1, 2 , 1 + 1, 2 , 1 + 1, 2 1 or 1 1, 2 + 1 as shown in fig. 2. Based on four modifications the reversible data hiding is defined and the reversible data hiding is defined based on this difference pair mapping as shown in fig. 3.
(1 1, 2 + 1)
(1, 2 )
1 1, 2 1 + 1, 2
1 + 1, 2 1
1 2
Fig. 2: The four modifications of the difference pair ,
considered and the difference pair 1 , 2 is calculated as
1 = and 2 = . The count of 1, 2 gives the two dimensional difference histogram shown in fig. 5. Here is th prediction of y, can be calculated using GAP predictor as:
1, > 80
1 + , (32,80]
2
=
1+3 , (8,32]
4
, [8,8]
4 +3 , [32,8)
4
4+ , [80, 32]
2
4 , < 80
(1)
Where 1 , . 5, 7 , 8 is neighbouring pixels of (, ) (see in fig.1), = 1 5 + 3 7 + 4 8 and =
.
1 2 + 3 4 + 4 5 represent the vertical and horizontal gradients, and = (1+4 ) + (3 5 ) The noise level
Fig. 3 Illustration of difference pair mapping
2 4
Table 1. Marked value of cover image (x,y) according to DPM and different cases of difference pair (1, 2)
Conditions on (1, 2 )
Operation in data embedding
Modification direction of the differencepair
Modification direction of the pixelpair
Marked value
1 = 1 and 2 > 0
Expansion embedding
Right
Left
+ ,
1 = 1 and 2 < 0
Expansion
embedding
Left
Left
,
1 = 0 and 2 0
Expansion embedding
Upperleft
Up
, +
1 < 0 and 2 = 0
1 = 0 and 2 < 0
Expansion embedding
Lowerright
Down
,
1 > 0 and 2 = 0
1 = 1 and 2 = 1
1 > 1 and 2 > 0
Shifting
Right
Right
+ 1,
1 < 1 and 2 < 0
Shifting
Left
Left
1,
1 < 0 and 2 > 0
Shifting
Upperleft
Up
, + 1
1 > 1 and 2 < 0
Shifting
Lowerright
Down
, 1
1 = 1 and 2 < 1

Embedding Algorithm
The embedding process was done as follows:

Divide the image into two nonoverlapping image blocks.

Take the first block and resize the block to the size of the whole image and do the following steps:

Except last two rows and last two columns, divide the image block into nonoverlapping pixel pairs.

The location map L is defined as, for the pixel pair with pixel values 0 or 255 the location map value will be one. Otherwise, it is zero.

The location map is losslessly compressed using arithmetic coding.

For the pixel pair , with L=0, compute the noise level and prediction of .

The data will be embedded according to the location map and pixel pair selection threshold T.

For a pixel pair , with location map L=1 the data embedding is not done.

If the pixel pair with L=0 and the noise level is greater than threshold T the data embedding is not done.

If L=0 and the noise level is less than threshold T the data embedding is done according to table 1. The last embedded pixel pair is kstar.

Record the LSB of the first (12 + 3 2 + ) of the image pixel to obtain the binary sequence S.

Embed S to remaining pixel pair, from kstar to kend.

Embed the LSB of the first (12 + 3 2 + ) pixels with auxiliary information shown below and repeat the embedding steps for the second block.


Pixel pair selection threshold: T(12 bits)

Last data embedded pixel pair: kstar ( 2 )

Length of compressed location map: bits.


Extraction Algorithm
The data extraction is done in the following steps:

Read the LSB of the first (12 + 3 2 + ) pixels of the marked image and determine the values of T, kstar, and kend.

From kend pixel pair the sequence S was extracted.

Then the marked image was divided into nonoverlapping blocks.

The first block was taken and the following extraction steps were done.

Divide the block into nonoverlapping pixel pairs.

The location map information was found out by arithmetic decoding.

The noise level and the pixel pair selection threshold T of the marked image was found out.

The data extraction would be done if noise level less than threshold and data extraction was based on table 2.

Then the data extraction was repeated for the second block.
Conditions on
( , )
Extracted data bit b
Recovered value
1 1,2 2
> 0
1
1
( , )
1
1, 2
2
< 0
1
1
( + , )
( = 0
1 2
0) (
1
= 1 0)
2
1
( , )
Table 2. Extracted data bit and recovered pixel pair.
1 2
( < 0
1 2
= 0) (
1
< 1 = 1)
2
2
( , )
( = 0
1 2
< 0) (
1
= 1 < 1)
2
( , + )
1
( > 0
1 2
= 0) (
1
> 1 = 1)
2
2
( = 1
1
1 2
= 1) (
1
= 2 = 2)
> 2
1 2
> 0
No
embedded
( 1, )
data bit
< 2
1 2
< 0
No
embedded
( + 1, )
data bit
< 1
1 2
> 1
No
embedded
( , 1)
data bit
> 2
1 2
< 1
No embedded
( , + 1)
data bit

Dataset 1 (b) Dataset 2


(c) Dataset 3
Fig. 4. Test images (a) Dataset 1 (b) Dataset 2 (c) Dataset 3


EXPERIMENTAL RESULTS
The improved reversible data hiding has been performed on the test images, including cameraman, tire, baboon etc. The average PSNR value after block division is higher than the PSNR value of the whole image. The variation of the PSNR value of the whole image to that of block division of dataset 1, dataset 2 and dataset 3 is shown in table 3, table 4 and table 5 respectively. The variation of embedding capacity versus PSNR is shown in fig. 6, fig. 7 and fig. 8. The improvement in PSNR value results in increasing the image quality. Also the embedding capaciy has been improved. By block division more pixel pairs can be utilized for embedding. The use of DPM and two dimensional difference histogram for embedding the redundancy in the pixel pair can be reduced and more pixels can be utilized for embedding. The plot of twodimensional histogram for the cameraman image is shown in fig. 5. The peaks in the histogram show the occurrence of the particular difference pair (1 , 2).
The PSNR value can be calculated as:
Fig. 5. Plot of two dimensional difference histogram of cameraman image Table 3. Variation of PSNR(dB) with embedding of dataset 1.
PSNR=1010
255 2 (2)
Length of input data (bits) 
PSNR (dB) of the whole image 
PSNR(dB) of two blocks 
Average PSNR (dB) after block division 
80 
67.0813 
67.1232 
68.673 
70.2237 

160 
67.0360 
67.0996 
68.63 
70.1757 

192 
67.0168 
67.0917 
68.62 
70.1598 

240 
66.9976 
67.0774 
68.604 
70.1308 

500 
66.8830 
67.0014 
68.49 
69.9862 

1000 
66.6530 
66.8755 
68.31 
69.7611 

5000 
65.9218 
65.9714 
67.071 
68.0641 
=1
mse =(1 ( ))
=1
( , , )2
(3)
Where , is the input image and , is the marked image.
Fig. 6. Variation of PSNR(dB) versus embedding capacity of dataset 1.
Table 4. Variation of PSNR(dB) with embedding of dataset 2.
Length of input data (bits) 
PSNR (dB) of the whole image 
PSNR(dB) of two blocks 
Average PSNR(dB) after block division 
24 
84.2956 
84.1871 
85.241 
86.2956 

32 
84.6819 
83.9911 
85.143 
86.2956 

48 
84.936 
83.9911 
84.936 
85.8817 

64 
83.8652 
83.9911 
84.573 
85.1562 

80 
83.7421 
83.9911 
84.3365 
84.6819 

160 
82.5390 
82.6783 
83.466 
84.2544 

320 
80.6136 
81.4172 
82.001 
82.5849 
Fig 7. Variation of PSNR(dB) versus embedding capacity of dataset 2.
Table 5. Variation of PSNR(dB) with embedding of dataset 3.
Length of input data (bits) 
PSNR (dB) of whole image 
PSNR (dB) of two blocks 
Average PSNR (dB) after block division 
16 
61.5491 
63.1307 
62.477 
61.8244 

48 
61.5447 
63.1275 
62.474 
61.8205 

80 
61.5411 
63.1239 
62.4704 
61.8170 

128 
61.5523 
63.1186 
62.4664 
61.8143 

160 
61.5287 
63.1155 
62.4633 
61.8112 

320 
61.5110 
63.0874 
62.4493 
61.8112 

500 
61.4883 
63.0832 
62.4339 
61.7846 

1000 
61.4232 
63.0451 
62.398 
61.7513 

5000 
61.1835 
62.6816 
62.063 
61.4451 
Fig. 8. Variation of PSNR(dB) versus embedding capacity of dataset 3.
IV. CONCLUSION
In this paper an improved reversible data hiding that is based on difference pair mapping and twodimensional difference histogram have been proposed. The data is embedded by modifying the pixel value. The twodimensional difference histogram will better exploit spatial redundancy. The proposed method based on block division of the image more data can be embedded. The experimental results show that this method provides good marked image quality.
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