
Research Article

Inverted Pattern in Inverted Time Domain for Icon Steganography

Rengarajan Amirtharajan
and
John Bosco Balaguru Rayappan


ABSTRACT

Information technologies and communications have pervaded our homes and business places. No matter how wellorganized and extensive the communication technology is there are always loop holes in the network and people who seek after the clandestine information to extract from these loop holes. The pandemic problem of security is still a raging problem as every solution compromises on one trait to heighten the other(s) according to the necessity of the hour. In this paper we suggest an algorithm where we have tried to retain all the three important banalities of secure communication: robustness, capacity and imperceptibility by using Haar Integer wavelet transform a Discrete Wavelet Transform (DWT) domain method in tandem with a cryptic scheme i.e. the direct binary or inverted binary embedding of data. Experimental results that compute the MSE and PSNR show that this algorithm caters to the need of the hour by delivering the high capacity with good imperceptibility. 




INTRODUCTION
In this age of booming communication and technology, everything is just one
phone call or e mail or a flight away. The free flow of information has behooved
the need for protecting this huge wealth of knowledge that has from time to
time helped tremendously in growth of science, technologies, civilizations and
mankind as a whole. With the invention of various algorithms for intelligent
storing, processing and transmitting of information ranging from minutiae procedures
to long exhaustive routines, many techniques have been developed for computer
security, information security and information assurance. The most prominent
amongst them is steganography (Stefan and Fabin, 2000,
Petitcolas et al., 1999; Rabah,
2004; Cheddad et al., 2010). It involves
communicating secret data in an appropriate multimedia carrier (Bender
et al., 1996; Rabah, 2004), e.g., image (Hmood
et al., 2010a; Amirtharajan and Balaguru, 2009;
Amirtharajan et al., 2011), audio (Zhu
et al., 2011), video (AlFrajat et al.,
2010) and text files (AlAzawi and Fadhil, 2010;
Yang et al., 2011; ShiraliShahreza
and ShiraliShahreza, 2008).
Cryptography another popular technique for information security involves scrambling
of data in an unintended format which would seem gibberish to any unintended
user (Schneier, 2007). No extraction can be done unless
the third party knows the secret code. However, it would invite tampering just
because of the existence of a secret message. That’s where steganography
has gained its impetus, with its basic constituents being a cover image, secret
data and a key (Stefan and Fabin, 2000; Petitcolas
et al., 1999; Rabah, 2004). The basic differences
between cryptography and steganography is given by Zaidan
et al. (2010) and Cheddad et al. (2010).
When combined it gives out a stego image which has high undetectability, is
robust and also has high capacity (Hmood et al.,
2010b) fulfilling requirements of the “Fridrich’s magic Triangle”
in information hiding (Stefan and Fabin, 2000; Padmaa
et al., 2011). Any files such as audio, video or image can be used
as cover (Bender et al., 1996; Rabah,
2004), cover being the carrier without secret data in it, after embedding
secret data in the cover; it is camouflaged with high level of imperceptibility
and dexterity to yield the stego image. Here capacity or payload would mean
the total amount of secret information that can be hidden in the stego image,
without any physical noticeability in the characteristics of the cover image
while robustness defines the limit of standing modifications before an adversary
can destroy the stego image. Within steganography, there are various techniques
or methods to embed the data in the cover image i.e., Substitution based (Amirtharajan
and Balaguru, 2009; Chan and Cheng, 2004), Transform
domain based (Thanikaiselvan et al., 2011a),
Spread Spectrum based (Amirtharajan and Balaguru, 2011;
Thenmozhi et al., 2011; Kumar
et al., 2011), Statistics based (Qin et al.,
2009; Zanganeh and Ibrahim, 2011), Distortion and
cover generation based (Xiang et al., 2011; Stefan
and Fabin, 2000).
The most prominent being Spatial Domain based Steganographic Techniques and
Transform Domain based Steganographic Techniques (Thanikaiselvan
et al., 2011a; Kumar et al., 2011),
Spatial domain based steganography include the Least Significant Bit (LSB) technique
(Amirtharajan and Balaguru, 2009; Chan
and Cheng, 2004), Pixel value differencing (Amirtharajan
et al., 2010; Thanikaiselvan et al., 2011b;
Padmaa et al., 2011) and more while the latter
includes DCT (Provos and Honeyman, 2003), DWT and especially
IWT (Thanikaiselvan et al., 2011a; EI
Safy et al., 2009). A detailed survey on steganography till 1999
is available in Petitcolas et al. (1999) whereas
a detailed survey on Information hiding in images, differences among cryptography,
Stegano graphy (Zaidan et al., 2010) and water
marking is available Cheddad et al. (2010). The
counter attack called steganalysis is described by Qin et
al. (2009) and Xia et al. (2009) and
various steganalysis review is detailed by Qin et al.
(2010).
A steganography technique is considered to be dependable when it can retain
the embedded data in spite of severe modifications done to it and not displaying
the payload contained in it. The most employed technique for this involves transforming
the cover image into another domain and embedding data in the transformed pixels
(Thanikaiselvan et al., 2011a; EI
Safy et al., 2009). Before transformation the cover image exists
in spatial domain, which is transformed into the frequency or time domain for
the coefficients and after embedding data in the transformed pixels, it is brought
back to the spatial domain. Thus, the underlying idea is, even if image is subjected
to modifications and is in worst cases transformed, the data will still be hidden
in the transformed pixels (Thanikaiselvan et al.,
2011a).
Transformation techniques include DCT, DWT and IWT, though DCT isn’t highly
preferred as it has low hiding capacity (Provos and Honeyman,
2003). Contemporary researchers use DWT, since it can be employed in compression
of formats JPEG2000 and MPEG4. Techniques that use DWT i.e., wavelet transform
based stego technique provides high capacity with the secret message embedded
into the high frequency and low frequency coefficients of the wavelet transform,
but provides less PSNR at a high hiding rate.
Cryptography in coalesce with steganography either through random walk (Luo
et al., 2008) or variable bit optimal embedding (Zanganeh
and Ibrahim, 2011) could be an effective solution to improve the complexity.
In this study we propose a new modified version of the methodology by Thanikaiselvan
et al. (2011a) which can embed a larger amount of data in Integer
Wavelet Transform (IWT) domain with high PSNR while combining with the inverted
pattern approach (Yang, 2008; Amirtharajan
and Rayappan, 2012) to improve the complexity.
Related works: The use of Wavelet transform will mainly address the
capacity and robustness of the information hiding system features. The Haar
Wavelet Transform is the simplest of all wavelet transform. In this the low
frequency wavelet coefficients are generated by averaging the two pixel values
and high frequency coefficients that are generated by taking half of the difference
of the same two pixels (EI Safy et al., 2009).
The four bands obtained are LL, LH, HL and HH which is shown in Fig.
1. The LL band is called as approximation band which consists of low frequency
wavelet coefficients and contains significant part of the spatial domain image.
The other bands are called as detail bands which consist of high frequency coefficients
and contain the edge details of the spatial domain image.
Integer wavelet transform: Integer wavelet transform can be obtained through lifting scheme. Lifting scheme is a technique to convert DWT coefficients to Integer coefficients without losing information. Forward Lifting scheme in IWT:
Step 1: Column wise processing to get H and L:

Fig. 1: 
Image and its transform domain bands 
Where, Co and Ce is the odd column and even column wise pixel values
Step 2: Row wise processing to get LL, LH, HL and HH, Separate odd and
even rows of H and L, Namely
LH = L_{odd}L_{even} 
(3) 
Where: 
H_{odd} 
= 
Odd row of H 
L_{odd} 
= 
Odd row of L 
H_{even} 
= 
Even row of H 
L_{even} 
= 
Even row of L 
Reverse lifting scheme in IWT: Inverse Integer wavelet transform is
formed by Reverse lifting scheme. Procedure is similar to the forward lifting
scheme (Thanikaiselvan et al., 2011a; EI
Safy et al., 2009).
LSB Embedding: Simple LSB embedding is detailed by Amirtharajan
and Balaguru (2009), Chan and Cheng (2004) and Janakiraman
et al. (2012). Consider a 8bit gray scale image matrix consisting
mxn pixels and a secret message consisting of k bits. The first bit of message
is embedded into the LSB of the first pixel and the second bit of message is
embedded into the second pixel and so on. The resultant stegoimage which holds
the secret message is also a 8bit gray scale image and difference between the
cover image and the stegoimage is not visually perceptible. This can be further
extended and any number of LSB’s can be modified in a pixel. The quality
of the image, however degrades with the increase in number of LSB’s. Usually
up to 4 LSB’s can be modified without significant degradation in the message.
Mathematically, the pixel value ‘P’ of the chosen pixel for storing
the kbit message Mk is modified to form the stegopixel ‘Ps’ as follows:
Ps = Pmod (P,2^{k})+Mk 
(7) 
The embedded message bits can be recovered by:
One method to improve the quality of the LSB substitution is Optimal Pixel
adjustment Process (OPAP) (Chan and Cheng, 2004).
Determination of embedding:
• 
Direct Binary embedding of data is done and the MSE (Mean
Square Error) calculated 
• 
Let it assumed to be X 
• 
Inverted Binary embedding of data is done and the MSE (Mean Square Error)
calculated 
• 
Let it assumed to be Y 
• 
Depending upon values of X and Y, If X > Y 
• 
Then the value is determined to be ‘1’ and if Y > X 
• 
Then the value is determined to be ‘0’ 
PROPOSED METHODOLOGY
The proposed block diagram of high capacity steganography system is given in Fig. 2 and 3. Preprocessing includes R, G and B plane separation and Histogram modification. Then Integer wavelet transform is applied to the cover image to get wavelet coefficients. Wavelet coefficients are randomly selected by using key2 for embedding the secret data. Key 2 is 8x8 binary matrix in which ‘1’ represents data embedded in the corresponding wavelet coefficients and ‘0’ represents no data present in the wavelet coefficients. Then the direct binary or inverted binary embedding of data is done in the respective coefficient. Key1(K1) is a decimal number varying from 1 to 4 and it will decide the number of bits to be embedded in the cover object. High capacity is achieved by varying the key1 (K1) value.
Embedding Algorithm 
Step 1: 
The cover image of size 256x256x3 pixels is selected 
Step 2: 
The respective planes are separated into R, G and B constituents 
Step 3: 
The required data file to be embedded is taken with each character taking
8 bits 
Step 4: 
Histogram modification is done in all planes, Because, the secret data
is to be embedded in all the planes, while embedding integer wavelet coefficients
produce stegoimage pixel values greater than 255 or lesser than 0. Then
all the pixel values will be ranged from 15 to 240 
Step 5: 
Each plane is divided into 8x8 blocks 
Step 6: 
Apply Haar Integer wavelet transform to 8x8 blocks of all the planes,
this process results in LL1, LH1, HL1 and HH1 sub bands 
Step 7: 
Using Key1(K1) calculate the Bit length (BL) for corresponding wavelet
coefficients (WC), here we used modified version of Bit length calculation
used by Thanikaiselvan et al. (2011a). Using
the following equation, we get the high capacity steganography 
Step 8: 
Using key2 select the position and coefficients for embedding
the ‘BL’ length data using LSB substitution. Here data is embedded
only in LH1, HL1and HH1 subbands. Data is not embedded in LL1 because they
are highly sensitive and also to maintain good visual quality after embedding
data. An example of key2 is shown below (This is Key B) 
Step 10: 
After doing required operations with K1 and K2, the direct
binary embedding of required data is done in the determine position and
coefficient and MSE calculated 
Step 11: 
Next inverted binary embedding of the same data is done in the same
position and corresponding MSE calculated

Step 12: 
Depending upon MSE values obtained Key3 (K3) is obtained with
value ‘1‘ when inverted binary is embedded and ‘0‘ when
direct binary is embedded. Thus K3 is also an 8*8 matrix consisting of
1s and 0s representing whether direct or inverted binary embedding of data
is done 
Step 13: 
Applying Optimal Pixel adjustment Procedure (OPAP) reduces the error caused
by the LSB substitution method 
Step 14: 
Take inverse wavelet transform to each 8x8 block and combine R,G&B
plane to produce stego image 
Extraction Algorithm 
Step 1: 
The corresponding stego image of 256*256 pixels is selected 
Step 2: 
The respective planes are separated into R, G and B constituents 
Step 3: 
Each plane is divided into 8x8 blocks 
Step 4: 
Apply Haar Integer wavelet transform to 8x8 blocks of all the planes,
This process results LL1,LH1, HL1 and HH1 subbands 
Step 5: 
Using Key1 calculate the Bit length(BL) for corresponding wavelet coefficients(WC),
using the ‘BL’ equation used in Embedding procedure 
Step 6: 
Using key2 select the position and coefficients for extracting the ‘BL’
length data 
Step 7: 
Then using key3, determine whether direct or inverted binary of the data
has been embedded and extract it then depending upon it 
Step 8: 
Combine all the bits and divide it in to 8 bits to get the text message 

Fig. 2: 
Block diagram for Embedding 

Fig. 3: 
Block diagram for extraction 
ERROR METRICS A performance measure in the stego image is measured by means of two parameters namely, Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The MSE is calculated by using the equation: where, M and N denote the total number of pixels in the horizontal and the vertical dimensions of the image X_{i, j} represents the pixels in the original image and Y_{i, j}, represents the pixels of the stegoimage. The Peak Signal to Noise Ratio (PSNR) is expressed as: RESULTS AND DISCUSSION
In this present implementation, Lena and baboon 256x256x3 color digital images
have been taken as cover images, as shown in Fig. 4a and 5a,
Stego lena (Thanikaiselvan et al., 2011a) in
Fig. 4b and 5b and Proposed Stego images
in Fig. 4c and 5c and tested with key1(keyJ)
and various key2s.
The effectiveness of the stego process proposed has been studied by calculating
MSE and PSNR for the Lena and Baboon digital image in RGB planes and tabulated.
In this analysis, Key2 is used for random selection of coefficients for embedding
data (in this analysis Key1 has been set as K1 = 1) and the results are tabulated
in Table 1 for various Key2 using the proposed method.
Table 1: 
Comparative Analysis for the proposed method key  1(K1)
= 1 

It’s evident that from Table 1, KeyJ provides high
capacity and KeyA provides low capacity, moreover proposed methodology gives
High PSNR over previous method (Thanikaiselvan et al.,
2011a). By keeping Key J constant various key 1 are tabulated in Table
2.
Complexity level estimation: For 8x8 pixels block case IWT, total number
of blocks (N) = 1024 Number of PRNG output for randomizing the 1024 blocks:
= NpR = N!/(Nr)! = 1024p1024 = 1024! 
As per DES, the complexity of each block = 2^64.
Either binary or inverted binary would be embedded hence complexity increases
by 2 and 3 out 4 sub bands are used and based on key 2 either 0 or 1 then 0.5.
Therefore the total complexity:
= (2^64)*(1024)! * (2) *3/4 *(.5) 
These embedding are carried out in transform domain and the security level estimation reveals the firmness of the proposed stego against hackers. CONCLUSION It has been observed that steganography provides excellent avenue for high payload combined with imperceptibility. Literature suggests that in several techniques robustness may have been compromised, however the proposed method gives high payload (capacity) in the cover image with very little error. The algorithm is robust, as there is an option of embedding data in the transformed domain and also the option of reducing the error by determining whether direct or inverted binary maybe embedded. Key1 and Key2 not only provide high security but also increased capacity with the wavelet transform. The drawback of the proposed method is the computational overhead. This can be reduced by high speed computers. Thus, it can be summarized as:
• 
PSNR is increased in this system with intelligent use of Key1,
Key2 and Key3 
• 
Because data is embedded in imperceptible areas that too in the transformed
domain, the stego image can hardly raise suspicion 
• 
Even if the stego image is transformed it would be difficult to determine
what data has been embedded without Key3 as either direct or inverted binary
would be embedded. Thus capacity, imperceptibility and robustness, all the
three requirements are catered to in this innovative algorithm 

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