INTRODUCTION

Being in the age of Electronics and Information Technology, majority of the information of large enterprises is maintained on machines in the form of digital data. This information is very sensitive and several mission-critical-applications depend upon this information (Stefan and Fabin, 2000). Any intruder who may get access to this information can not only leak the information but also tamper this information which can lead to malfunctioning of the mission-critical systems. This certainly can create havoc to the future of such organizations and nations. So, information security plays a vital role in today’s info driven scenario (Amirtharajan and Balaguru, 2009).

Petitcolas et al. (1999), Rabah (2004) and Cheddad et al. (2010) elucidated steganography is a method of hiding data into another cover media in such a way that only the receiver knows the presence of secret data in the cover and retrieve it. Schneier (2007) explained various encryption methods for data security whereas steganography is a very secure method, because the intruder has no idea of presence of a secret data (Stefan and Fabin, 2000; Zaidan et al., 2010). Bender et al. (1996) discussed various techniques of data hiding, where some of the methods have been used from long past included usage of wax to cover data engraved on wooden pieces and also use of invisible inks (Petitcolas et al., 1999). So many authors highlights that (Amirtharajan et al., 2010b; Hmood et al., 2010a, b; Cheddad et al., 2010) rather than robustness, steganography concentrates more on the payload i.e., the amount of data to be embedded. Another important aspect of steganography is the imperceptibility (Aura, 1996; Wang et al., 2001; Chan and Cheng, 2004; Zanganeh and Ibrahim, 2011), i.e., the stego cover has to maintain its original quality even after the secret data is embedded into it (Bender et al., 1996, 2000; Amirtharajan and Balaguru, 2009, 2010).

Though very secure, there are analytical techniques by which the presence of secret data can be found out using statistical difference between the cover and stego objects. To fight against this type of analysis (Fridrich et al., 2001; Wang and Wang, 2004; Qin et al., 2010), two things should be kept in mind. First, while embedding the data into the cover, avoid conspicuous parts. Secondly, the embedding efficiency has to be improved.

The image based steganography (Hmood et al., 2010a; Cheddad et al., 2010; Amirtharajan and Balaguru, 2009, 2010; Amirtharajan et al., 2010a-c) requires an image as the media to embed the secret data into. The secret data can be embedded by modifying or changing the pixel values or by changing the intensity value of the pixel. Least Significant bits LSB (Cheddad et al., 2010; Amirtharajan and Balaguru, 2009, 2010; Amirtharajan et al., 2010a-c), Pixel Indicator (Gutub et al., 2008; Gutub, 2010; Parvez and Gutub, 2008; Upreti et al., 2010; Amirtharajan and Balaguru, 2010; Amirtharajan et al., 2010a-c) and Pixel Image intensity variation (Park et al., 2005) based are few techniques for image steganography.

The most well-known Steganographic technique in the data hiding field is least-significant-bits (LSBs) substitution (Cheddad et al., 2010; Amirtharajan and Balaguru, 2009, 2010; Amirtharajan et al., 2010a-c). This method embeds the fixed-length (maximum of 3) secret bits in the same fixed-length LSBs of pixels but it generally causes noticeable distortion(if it is more than 3). Several adaptive methods for steganography have been proposed to reduce the distortion caused by LSBs substitution (Zanganeh and Ibrahim, 2011). Chan and Cheng (2004) proposed one such method to reduce distortion called Optimal Pixel Adjustment Process [OPAP]. On the other hand the adaptive methods vary the number of embedded bits in each pixel and they possess better image quality than other methods. However, this is achieved at the cost of reduction in the embedding capacity.

A state-of-the-art survey on current digital image steganography, steganalysis methods along with some common standards and guidelines drawn from the literature are available in Cheddad et al. (2010). It also classifies steganography based on covers like video (Al-Frajat et al., 2010; Amirtharajan et al., 2010d), audio (Amirtharajan et al., 2010d), text (Shirali-Shahreza and Shirali-Shahreza, 2008; Al-Azawi and Fadhil, 2010) and image whereas another classification is based on the modification on the covers.

Gutub et al. (2008) and Gutub (2010) proposed Pixel Indicator based color image steganography, where in the last two bit of the indicator plane decides, the remaining planes are data channel or not. Amirtharajan et al. (2010a-c) exploited Pixel indicator method by several variations. In all the proposed, the authors have taken Pixel Indicator as a base to identify suitable plane of a pixel for embedding, later how many bits are used to embed would be decided by Excess 3 value of the indicator values (Amirtharajan et al., 2010b). In another variation from the same author (Amirtharajan et al., 2010c) pixel value differencing (Park et al., 2005; Wu et al., 2005) decides the number of bits along with pixel indicator. Furthermore one more Pixel indicator method has been considered by Amirtharajan et al. (2010a) where in the authors proposed a method to increase the robustness by introducing a factor E as an option to select the bit position in a pixel to plant the message to be concealed.

Another classification in image steganography is methods using raster scan Chan and Cheng (2004) or random scan (Amirtharajan and Balaguru, 2009, 2010; Padmaa and Venkataramani, 2010; Provos and Honeyman, 2003; Luo et al., 2008). The former visits the entire pixel like regular TV scanned lines from left to right, top to bottom where the later by traversing all the pixels by pseudo random path. Aura (1996) proposed a random image steganography. Its drawback is huge time in computing the suitable target pixel for embedding. Amirtharajan and Balaguru (2009, 2010) proposed two such random traversing paths, offering random traversing LSB embedding method but fails to improve its randomness while embedding. There are papers available in literature for multiuser secret sharing using visual cryptography proposed by Naor and Shamir (1994) but it’s not hiding the aspect of sharing the cryptographic share among users. No authors in the recent past have reported multi user secret sharing using pixel indictor methods.

Hence, in present study, an optimistic maiden effort has been taken to propose a method which improves the randomness while embedding, by adapting pixel indicator method for multi user and encryption prior to embedding. This makes the hacker to think more to device a method to crack the system. Later after embedding OPAP module has been put upon to improve the quality of the stego cover.

PROPOSED METHODOLOGY

In steganography, according to the Magic triangle, robustness, security and capacity are the three characteristics which are of prime importance (Stefan and Fabin, 2000). For an ideal steganographic algorithm, all the three characteristics altogether can never be satisfied. Until now, only one user could use steganographic conditions in a channel. Whereas, this study explores the possibility of having “Two Users” using this technique, where two different keys would be given to each user after symmetric encryption through DES. To improve the level of security a steganographic method “Pixel Indicator Technique” is included along with a blend of cryptography. The research done on “Two Users” can further optimize the data capacity, compared to a single user in the earlier techniques in steganography.

This methodology enables us to achieve the following:

The procedure applied here involves raw secret data from two distinct users.

Fig. 1:	Block diagram of the proposed methodology

Fig. 2:	Flowcharts for embedding

In the Encryption process, the data T1 and T2 is encrypted using two distinct keys K1 and K2 known by the individual users, respectively using (Schneier, 2007) Symmetric Data Encryption Standard (DES) the block diagram representation of the proposed method is shown in Fig. 1 and Flowchart for Embedding and Extraction in Fig. 2 and 3. The encoded data D1 and D2 is enclosed inside a cover image using Pixel indicator method. a color image comprises of pixels having three 8 bit channels RED, GREEN, BLUE in each pixel.

Fig. 3:	Flowcharts for extraction

Table 1:	Function of the proposed two user pixel indicator method

In pixel indicator method, one of the three channels is selected as an indicator channel, whereas the remaining two channels are used to embed the encoded data. Depending on the last two bits of the indicator channel data to be embedded in the other two channels is conferred. In this scenario, there are four possibilities to be considered are shown in Table 1. If the last two bits,

The randomization is increased by using Pixel value differencing technique (Park et al., 2005) as the method itself decides as to how many bits are to be embedded in the respective channels. Also, the imperceptibility and robustness of the system can be achieved here by hiding the secret data with more randomization.

During retrieving, the data should be first extracted from the channel i.e., the image and then original data is decrypted by using the same algorithm which is used during the encryption. This retrieving process can be done only if the individual opponents know their respective keys k1 and k2 and hence it makes the process more secure are as shown in the Fig. 1. As mentioned above the security of the process is enhanced since the user has to extract and then decrypt the message to get the required data.

Mathematical model for 2 user, pixel indicator: Two flavors of secret data:

3 Planes:

Embedding procedure: Let the cover image be C with McxNc pixels.

Let ‘k’ be the number of LSBs derived from PVD to be replaced in cover pixels.

Let each secret message be a matrix Mu, where each element of Mu is made up of k bits. Then we can denote the message to be embedded in the ith row, jth pixel as mu (I, j).

Let the stego image also be split into 3 planes Rs, Gs and Bs corresponding to R, G and B planes of cover image.

Let the indicator I (I, j) be defined for each pixel as the last two bits of R (I, j),

I (I, j) = R (I, j) mod 4

Stego image’s planes can be denoted as:

Retrieval procedure: Let the indicator I (I, j) be defined for each pixel as the last two bits of Rs (I, j),

I(i,j) = R(i,j) mod 4

The messages mu (I, j) can be extracted from pixels in stego image as:

THE PROPOSED METHOD ALGORITHMS

Case-1: Two users PVD with Tri-Colour random image steganography
Embedding Algorithm method 1:

Recovery algorithm method 1:

Case-2: Two user PVD with Custom-indicator-plane Tri-colour random image steganography
Embedding Algorithm method 2:

Recovery algorithm method 2:

Case-3: Two user PVD with Cyclic-indicator-plane Tri-colour random image steganography
Embedding Algorithm method 3:

Recovery Algorithm method 3: