 Open Access
 Total Downloads : 140
 Authors : Arunvinodh C, S. Poonkuntran
 Paper ID : IJERTV5IS110332
 Volume & Issue : Volume 05, Issue 11 (November 2016)
 DOI : http://dx.doi.org/10.17577/IJERTV5IS110332
 Published (First Online): 29112016
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
A 3Way Tensor Framework based Blind Steganalysis using Cyclic Ensemble Classifier
C. Arunvinodh
Research Scholar,
Computer Science and Engineering Dept.
JJT University, Rajasthan
Dr. S. Poonkuntran
Professor, Computer Science and Engineering Dept. Vellamal College of Engineering and Technology, Madurai, India
AbstractThis manuscript intends a Blind Steganalysis framework which can be applied irrespective of domain specific steganography algorithms. Extracting image features and classification are the significant process in Blind Steganalysis. The framework proposed uses a 3way tensor model to extract the image features which is important for estimating the embedded change in stego image. Ensemble classification is depicted here uses forward and backward cyclic matricizing which improves the detection accuracy in most of the steganography algorithms. The experimental results evaluated on 3000 images which significantly reduce the false acceptance rate and false rejection rate. Our proposed framework produces an Average false acceptance rate of 1.99% and average false rejection rate of 0.78% based on the pay load when tested with spatial domain steganographic algorithms proposed in [12], [16], [29], [30] and transform domain steganography algorithms such as JPHS, JSTEG, MBS1, MMx, and nsF5.
Index Terms: Spatial Domain, Transform Domain, Steganalysis, Tensor, Ensemble Classifier, Payloads

INTRODUCTION
Steganalysis is the art of determining concealed data in images. The Steganalysis techniques are classified into two categories. 1. Explicit Steganalysis 2. Blind Steganalysis. Explicit Steganalysis are intended for a targeted Steganographic technique [13], [5] where as in blind steganalysis technique focuses on the stego image irrespective of the steganographic algorithms. It has been acknowledged that most of the research findings paying attention on recognizing the embedded data instead of extracting the data [1],[2],[3],[5],[6].
Steganography can be done in two major domains. 1. Spatial domain. 2. Transform Domain. It has been recognized that design of steganalysis algorithm is focused mainly on Transform Domain [2], [4]. The algorithm which has been intended for Transform domain is moderately working for Spatial Domain [2], [3], [14]. Therefore the highlight of this manuscript is to develop a blind Steganalysis which does not bother about domain specific steganographic algorithms.
Tomas Pevny etal [4] focused their assumptions more on Transform domain. The investigation proposes to concentrate more on spatial domain steganography algorithm as well as JPEG Domain, it is difficult to predict how well the result will compare to Tomas Pevny results. Jan Kodovsky and Jessica Fridrich [1] assumed that both training and testing images were generated based on uniformly distributed payloads. This
assumption leads to a development of a steganalysis algorithm which has to identify any distribution (uniform or non uniform) of stegopayloads. The validation is done by the literature survey based on nonuniform distribution of stego payloads as follows. T.Pevny etal.[15] highlighted about the challenge of steganalysis researchers for advanced content adaptive steganographic methods. Even though J.fridrich etal.
[2] proposes a universal steganography detector which successfully attacked LSB matching revisited algorithm (LSBMR) proposed by LUO etal.[16]. Edge adaptive Image Steganography Based on LSB Matching Revisited (EALSBMR)[17] algorithm was not successfully attacked by [2] because of the adaptive methodology. Shunquan Tan etal. [18] proposes Targeted steganalysis of EALSBMR and it was successful only for the proposed steganography algorithm. There are many adaptive steganography algorithm [39],[40],[41],[42] where an embedding redundancy in LSB matching to select modification direction and takes the dependency of neighbouring pixels into consideration. Since the neighboring pixel dependency is considered the universal steganalysis may be a challenging part in my research [18],[36],[37],[38]. A combined spatial domain embedding and transform domain embedding makes difficulty in the attack [19].Based on the above justification, this manuscript proposes a unique frame work based on 3 way tensor model which will accompany adaptive steganalysis as well as steganalysis of uniform payload distribution. Also the frame work satisfies the requirement of steganalysis in the spatial domain as well as the JPEG Domain
This paper is organized as follows. In section II, a Frame work has been proposed using 3way tensor model and we discussed the details of SCI, Forward cycling and backward cycling of matrices and bit change rate estimation. The Results and Discussion claims the successful working of the framework by analyzing in various test bed created based on the steganography algorithms which is mentioned in the section III. Finally the conclusion is summarized in section V.

FRAME WORK
In our system, the frame work proposed in figure 1 clearly shows the importance of tensor representation. The mathematical model of this frame work proposed is adopted from [20], [21] and [22] which give the foundation of tensor representation and manipulation
Sliced Cover Image (SCI)
Sliced Cover Image (SCI)
SCI flat Slice C(i, : ,:)
SCI Cross Slice C(:, j ,:)
SCI anterior Slice C(:, : ,k)
SCI flat Slice C(i, : ,:)
SCI Cross Slice C(:, j ,:)
SCI anterior Slice C(:, : ,k)
SCI Columns fiber C(:, j ,k)
SCI Rows fiber C(i, : ,k)
SCI Tubes fiber C(i, j ,:)
SCI Columns fiber C(:, j ,k)
SCI Rows fiber C(i, : ,k)
SCI Tubes fiber C(i, j ,:)
Tensor Slice and Fibre Conversion
Tensor Slice and Fibre Conversion
Forward and Backward cyclic 3 way tensor
Cyclic Ensemble Classifier
Figure 1: Proposed Frame work

SCI Generation
The first step in SCI Generation based on [35] is to partition the image into N slices, where N is denoted as the number of bit slices. If the image is composed of Nl bit slices, ranging from slice 0 for least significant bit to slice Nl for the most significant bit. In terms of N bit slices, slice 0 contains all the lowest order bits in the bytes comprising the pixels in the image and slice N1 contains all the high order bits. Therefore by separating the image into bit slices, we immediately have a method of identifying more important and less important information which is suitable for extracting the image features.
The image can be divided into bit slices by the following steps.

Let I be an image where every pixel value is nbit long

Express every pixel in binary using n bits

Form out of I n binary matrices
where the ith matrix consists of the ith bits of the pixels of I.


Tensor Slice and Fibre Conversion
Let be a Bit Sliced Tensor of dimension
1 Ã— 2 Ã—Â· Â· Â·Ã— . (1)
The order of is N. The nth dimension (or mode or way) of C
is of size Cn. Let
(, , ) acquiesce the ith SCI flat slice,
(: , , : ) the j th SCI Cross slice, and
(: , : , ) the kth SCI anterior slice
(: , , ) yields a column fibers,
(, : , ) yields a row fibers, and
(, , : ) yields a socalled tube fibers as shown in Figure 1.
Typically, a tensor is matricized such that all of the fibers associated with a particular single dimension are aligned as columns of the resulting matrix. In other words, we align the fibers of dimension n of ensor to be the columns of the matrix. The resulting matrix is typically denoted by C(n). The columns can be ordered in many ways. As discussed in [21], the ordering can be given as
(1), (2), (3)
Figure 2: Backward cyclic matricizing a threeway tensor.
{c1, . . . , cL} = {n 1, n 2, . . . , 1, N, N 1, . . . , n + 1}, (2)
and this ordering is named as backward cyclic. As per [23], the ordering is specified as follows
{c1, . . . , cL} = {n + 1, n + 2, . . . , N, 1, 2, . . . , n 1}, (3)
and this ordering is mentioned as forward cyclic or fc for short. This framework uses both backward and forward cyclic which is helpful for identifying the bit change rate.
Based on the matricizing process an Nthorder tensor is represented as follows
R1Ã—2Ã—Â·Â·Â·Ã— (4)
(4), (5), (6)
The detector is designed with the help of an ensemble classifier method known as bagging which is proposed in [3],[43]. Our proposed framework uses bagging method to create classifiers based on forward and backward cyclic matricizing. To describe our cyclic ensemble classifier, we introduce the following modified notations.
The backward cyclic ensemble classifier is denoted as , where = 1 . . . Then 3 fibers for backward cyclic
ensemble classifier are denoted as 1 , 2 , 3
The Forward cyclic ensemble classifier is denoted as , where = 1 . . . Then 3 fibers for backward cyclic
ensemble classifier are denoted as 1 , 2 , 3 .
Figure 3. Forward cyclic matricizing a threeway tensor.
The matrix Unfolding is represented as follows.
The prediction of the complex classifier for Backward cyclic and Forward cyclic Ensemble Classifier can be represented as follows
1 () = (=1 1 ( )) (9)
2 () = (=1 2 ( )) (10)
3 () = (=1 3 ( )) (11)
()
R1Ã—(+1+2Â·Â·Â·12Â·Â·Â·1)Ã—2Ã—Â·Â·Â·Ã— (5)
The prediction of the complex classifier for Forward cyclic and Forward cyclic Ensemble Classifier can be represented as follows
Which contains the element 12Â·Â·Â· at the position with row
number and column number equal to
1 () = (=1 1 ( )) (12)
(n+1
1) +2
+3
.
1
2
. . 1
+ (+2
2 () = (=1 2 ( )) (13)
1) +3 +4 . 1 2 . 1 + (
3 ( ) = (
3 ( )) (14)
1) 1 2 . 1( 1 1) 2 3 1
(6)
=1

Ensemble Classifier
Forward cyclic and backward cyclic matricizing creates 6 matrices from 3way tensor. Rate of intensity change in a particular region of an image always have slight variation. Therefore by analyzing the change in the bit rate, image features can be extracted. To estimate the embedding change rate we use a methodology called as hamming distance.
Let us take the same elements which are shown in the previous section. The element 12Â·Â·Â· at the position with row number and column number equal to
( 1 . . . + (
Algorithm 1 Backward Cyclic Ensemble Classifier
1. For =1 to 3

Initialisation of the training set D
3. for m = 1, …, M

Creation of a new set Dm of the same size D by random selection of training examples from the set D

Learning of a particular classifier


Bm: R
by a given machine learning algorithm
based on the actual training set Dm.
4. Compound classifier B is formed as the aggregation
n+1
) +2
+3
1 2
1
+2
of detailed classifiers Bm: m = 1, …,M and an
1) +3 +4 . 1 2 . 1 + (
1) 1 2 . 1( 1 1) 2 3 1
(7)
example is classified to the class in accordance with the number of votes obtained from particular classifiers Bm. and thus it is represented as
which is XORed with row number +1 and the
corresponding column number is
(n+2 1) +3 +4 . 2 3 . . 2 + (+3 1) +4 +5 . 2 3 . 2 + (
(, ) = ( ( , ))
=1
1) 2 3 . 2( 2 1) 2 3 1
(8)
The number of change in bits can be estimated by counting the number of ones. This process can be repeated for all unfolded matrices.
Algorithm 2 Forward Cyclic Ensemble Classifier 1. For =1 to 3

Initialisation of the training set D 3. for m = 1, …, M

Creation of a new set Dm of the same size D by random selection of training examples from the set D

Learning of a particular classifier

Bm: R
by a given machine learning algorithm
based on the actual training set Dm.
5. Compound classifier F is formed based on the aggregation of detailed classifiers Fm: m = 1, …,M and an example is classified to the class in accordance with the number of votes obtained from particular classifiers Fm. and thus it is represented as
which is shown in Table II. The FAR and FRR is very low at an average of 1.7% and 1.2%.
Table I: Detection Analysis of Spatial Domain Algorithms
Algorithm
Average Detection Rate
Spatial Domain
Average False Acceptance Rate
Average False Rejection Rate
[12] 77.52%
1.6%
0.05%
[16] 80.1%
312%
0.67%
[29] 74.76%
4.55%
3.22%
[30] 78.03%
3.21%
3.74%
Table II: Detection Analysis of Transform Domain Algorithms
Algorithm
Average Detection Rate
Spatial Domain
Average False Acceptance Rate
Average False Rejection Rate
JP
Hide&Seek (JPHS) [31]
88.23%
0.042%
0
Jsteg [28]
94.51%
0.86%
0.033%
MBS1 [27]
89.39%
0.43%
0.006%
MMx [32]
86.12%
1.08%
0.56%
nsF5 [33]
85.28%
1.42%
0.62%
Algorithm
Average Detection Rate
Spatial Domain
Average False Acceptance Rate
Average False Rejection Rate
JP
Hide&Seek (JPHS) [31]
88.23%
0.042%
0
Jsteg [28]
94.51%
0.86%
0.033%
MBS1 [27]
89.39%
0.43%
0.006%
MMx [32]
86.12%
1.08%
0.56%
nsF5 [33]
85.28%
1.42%
0.62%
( , ) = ( ( , ))
=1


RESULTS AND DISCUSSIONS
Since we unfolded the matrix we organized our database as equally distributed training and testing set. Each training and testing set consist o 1 (), 2 () , 3 () , 1 (),
2 () and 3 (). The framework proposed is effectively
evaluated in 3000 images in the database provided by BOWS2[34]. To construct the steganalyzer an approximation of an unknown function :
{, } where D is the set of images of stego and cover image and = {1 , . .  is the set of predefined groups. The value of the function for a pair
, is true if the image belongs to group . The function : {, } which approximates is called a classifier.
For the entire algorithm the stego image bit change scatters a lot when it compared with the input image bit change. In this paper we use False Acceptance Rate (FAR) as the framework incorrectly detected as the stego image and False Rejection Rate (FRR) as the framework incorrectly rejected that the image is a cover image. Table I and II clearly shows that the average detection rate increases as the payload increases. The FAR and FRR increases only when the payload increases from 2.3 as shown in the figure
1 and 2. Almost for all unfolded matrix the average detection rate is 65% irrespective of the payload. The framework is applied to spatial domain steganography algorithms based on [12], [16], [29] and[30] which is shown in Table I. The FAR and FRR is very low at an average of 2.4% and 3.3%. The framework is applied to Transform Domain steganography algorithms based on JPHS [31], Jsteg [28], MBS1[27], MMx[32], nsF5 [33]

CONCLUSION
In this paper, a novel framework has been proposed based on 3 way tensor model. As a start of this invention, excellent results obtained for algorithms with an average detection rate of 65% irrespective of payloads. Based on the spatial domain an average detection rate achieved is 77.6% and for Transform domain we achieved is 88.71%. Also the framework was successful in both uniform and non uniform distribution of Stegopayloads. The disadvantage was the low average detection rate for [29] & [30] for spatial domain steganography algorithms and [27], [32] & [33] for transform domain steganography algorithms.
To increase the average detection rate and to decrease the Average false acceptance rate and average false rejection rate, the proposed framework can be applied to regions in an image.

REFERENCES

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Table I
Training and testing details with payload increasing from 0.5 to 4.5 for 1 (), 2 () and 3 ()
payloads
()
()
()
FAR
%
FRR %
DA
DR
%
FAR
%
FRR %
DA
DR
%
FAR
%
FRR %
DA
DR %
0.5
0.0
0.000034
1
0.8
1
0.00278
2
1.4
0
0.0033343
7
10.4
1.5
0.01
0.01461
4
17.5
2.01
0.01461
4
23.9
1.765
0.0151643
9
27.9
2.5
0.1
0.15275
16
69.1
2.1
0.15275
20
73
3
0.1533043
25
77
3
1.0
0.92694
16
72.2
5
0.92694
22
79.34
8
0.9274943
27
92.34
3.5
2.0
1.86436
16
72.4
8
1.86436
22
82.1
10
1.8649143
27
93.12
4
5.0
5.16743
19
74.3
12
5.16743
25
82.4
13
5.1679843
30
93.67
4.5
10.0
11.778
19
82.7
19
11.778
27
84.54
22
11.778554
32
94.55
Table II
Training and testing details with payload increasing from 0.5 to 4.5 for 1 (), 2 () and 3 ()
payloads
()
()
()
FAR
%
FRR %
DA
DR %
FAR
%
FRR %
DA
DR %
FAR
%
FRR %
DA
DR %
0.5
0
0.0033343
7
10.4
0
0
11
12.0789
1
0.00278
0
0.1544
1.5
1.765
0.0151643
9
27.9
1.3
0
14
39.543
2
0.01461
2
22.6544
2.5
3
0.1533043
25
77
2
1.3445
26
58.23
2
0.15275
18
71.7544
3
8
0.9274943
27
92.34
3.22
1.6768
29
85.66734
5
0.92694
20
78.0944
3.5
10
1.8649143
27
93.12
4
1.92354
31
94.4524
6
1.86436
20
80.8544
4
13
5.1679843
30
93.67
6.78
2.13
34
95.3489
8.3
5.16743
23
81.1544
4.5
22
11.778554
32
94.53
7.566
4
39
96.2289
11
11.778
25
83.2944

(b)
Figure 1: Detection rate on payloads for ()1 (), ()2 ()
3 1

(b)

(c) (d)
Figure 2: Detection rate on payloads for ()3 (), ()1 (), ()2 () and ()3 ()
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C.Arunvinodh received his B.E degree in Electrical and Electronics Engineering from Annamalai University, India in 2002 and the M.Tech. degree in Computer and Information Technology from Manonmaniam Sundaranar University, India in
2005. He is currently doing his Ph.D in JJT University, Rajasthan. He is having 9 years of experience in teaching. He has published papers in 1 international journal, 4 International conference and 2 National Conferences. His research interests include Information Security, Digital image processing and Biometrics.
Dr. S. Poonkuntran received B.E in Information Technology from Bharathidasan University, Tiruchirapalli, India in 2003, M.Tech in Computer and Information Technology from Manonmaniam Sundaranar University, Tirunelveli,
India in 2005 and Ph.D in the department of computer science and engineering, Manonmaniam Sundaranar University, Tirunelveli, India in 2011. He is having 10 years of experience in teaching and research. He is a life time member of IACSIT, Singapore, CSI, India and ISTE, India. He has published papers in 4 national conference, 22 international conferences, 1 national journal and 10 international journals on image processing, information security and soft computing. Presently he is working on Computer Vision for under water autonomous vehicles and Information Security for Healthcare Information Systems. His areas of research interest include digital image processing, soft computing and energy aware computing in computer vision.
