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 (Al-Frajat et al., 2010) and text files (Al-Azawi and Fadhil, 2010; Yang et al., 2011; Shirali-Shahreza and Shirali-Shahreza, 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 un-detectability, 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 notice-ability 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

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 8-bit 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 stego-image which holds the secret message is also a 8-bit gray scale image and difference between the cover image and the stego-image 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 k-bit message Mk is modified to form the stego-pixel ‘Ps’ as follows:

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:

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 key-2 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 co-efficient. Key-1(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 key-1 (K1) value.

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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:

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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 stego-image. The Peak Signal to Noise Ratio (PSNR) is expressed as:

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