CHAPTER 7 Steganography based on Random pixel · PDF file image based steganography is selecting of technique that can fulfill the basic requirements in developing efficient steganographic

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CHAPTER 7

Steganography based on Random pixel

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7.1 Introduction

An ideal Steganography technique embeds data into images in a way that forms

modified images which are visually imperceptible. As the application domain of

embedding data in image source becomes broaden, data hiding techniques are

designed in terms of security, capacity and imperceptibility. The performance of a

steganographic system can be measured using some properties of which the most

important property is the statistical undetectability of the data, which exhibits

difficulty in determining the existence of a hidden message. Another associated

measure is the steganographic capacity, where the maximum information can safely

embedded in a cover-image without having statistically detectable objects (Hamid et

al., 2013).

LSB based technique enables high capacity of data embedding and also maintaining

the visual perception of the stego-image. This method is probably the easiest way of

hiding information in an image and yet it is surprisingly effective (Johnson and

Jajodia, 1998). As stated by Jain et al., (2012b) LSB steganography is described as

follows: if the LSB of the pixel value I(i, j) is equal to the message bit m to be

embedded, I(i, j) remain unchanged; if not, set the LSB of I(i, j) to m. The message

embedding procedure is described using equation 7.1 as stated below,

(7.1)

where LSB(I(i, j)) stands for the LSB of I(i, j) of the cover-image, m is the bit to be

embedded and IS (i, j) is the pixel‟s LSB value of the stego-image.

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Such approach employs lossless compression in its mechanism that preserves the

quality and also increases the quantity of the secret data. LSB methods typically

achieve high capacity but unfortunately LSB insertion is vulnerable to attacks (Pujari

and Mukhopadhyay, 2012). In LSB steganography framework, the technique for

embedding the message may be known to an attacker and so it is generally not

considered as good practice to rely on the secrecy of the algorithm itself. The simple

LSB method can be enhanced with often much more sophisticated approach.

A digital image is defined as an arrangement of numbers and such numbers usually

stand for different light intensities in different parts of the image. In a color scheme,

the number of bits represents the bit depth and this basically refers to the number of

bits assigned to each pixel (Morkel et al., 2005).

Figure 7.1: RGB color space showing primary colors and gray value

The RGB (Red-Green-Blue) color model has three basic primary (basic) colors: red,

green and blue. All other colors are obtained by combining them (Carvajal-Gamez et

al., 2009; Kaur et al., 2010). In RGB color space, the colors with „P‟ are the primary

colors and the dashed line indicates where to find the grays, going from (0, 0, 0) to

(255, 255, 255) as shown in the figure 7.1. Images consist of pixels with contributions

from primary colors (red, green and blue) adding to the total color composition of the

pixel (Johnson and Jajodia, 1998). Each pixel typically has three numbers associated

with it, one each for red, green, and blue intensities, and these value ranges from 0-

255 (Gonzalez and Woods, 2002; Hosmer, 2006). Depending on the depth of color

desired in the final image, each component is represented by a separate number of

bits. When represented as decimal contributions, a value of (255, 0, 0) would describe

a 100% red pixel. By mixing the contribution of each component different colors can

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be represented. Value mixtures such as (31, 187, 57) can result in a dark green while

(255, 255, 0) represents pure yellow.

(255, 255, 0) (254, 255, 0)

Figure 7.2: Red plane LSB Modification

When any specific color is viewed closely, single digit modifications to the

contribution level are imperceptible (i.e. a pixel with a value of (255, 255, 0) is

indistinguishable from (254, 255, 0)) as shown in figure 7.2 that illustrates the impact

of modifying one bit in the red contribution. One of the most important properties of

image based steganography is selecting of technique that can fulfill the basic

requirements in developing efficient steganographic process. If the LSB‟s of the red

plane is changed only, then such change does not have a noticeable impact on the

color of a pixel as seen in figure 7.2.

The LSB based data embedding can differ in the way of data hiding process and

different variations of LSB insertion exist. Information bits can be embedded in

image‟s LSB sequentially or randomly at fixed place or by random scattering

throughout the cover-image (Cheddad et al., 2009; Hamid et al., 2013). In the

sequential embedding scheme, the LSBs of the image are replaced by the message bit

sequentially (Tomar, 2012). Embedding depends on the length of the message and

thus the position of the pixel where the secret message is hidden is modified while the

rest remain untouched. But the disadvantage of sequential embedding is that the

message is encoded into the cover-image sequentially (ordered), so the clusters of bits

embedded can be found that results in abrupt changes in the bits statistics and this can

result in detection (Zhang et al., 2006).

For random case, there are no such clusters because the embedded bits are scattered

into the cover-image, so the detection process is not expected to be as effective as for

the sequential case. Randomizing the embedding process is one of the variations of

LSB approach where the secret bits can be placed into the pre-selected pixels of the

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cover-image. Thus the bits of secret message bits will not be beside in one part, but

instead randomly dispersed throughout the image. In such embedding scheme, the

message bits are spread throughout the whole image using a random sequence to

control the embedding sequence (Hamid et al., 2013; Tomar, 2012). By randomizing

the embedding approach, the method to estimate the statistical properties of the image

used can be effectively disabled. Such approach of hiding in a randomized manner is

quite appealing and proves effective. In this process, a pseudorandom number

generator (PRNG) is used to select random pixels of the cover-image for hiding the

secret data. A chosen key can be inserted into a PRNG which will determine a

sequence of random numbers (Manchanda et al., 2007). These numbers indicate the

pixels in the cover-image where the LSBs are to be changed. In such case without the

secret key, it becomes significantly difficult for the attacker to figure out the target

pixels (Weng et al., 2009; Cole and Krutz, 2003). This makes the system more secure

because the receiver of the message must know the key in order to determine the

locations of the bits of the hidden message. So, in case the stego-image is known,

embedding the message in a random manner would not affect the security as long as

the stego-key remains unknown to the attacker. The secret key is the password used to

seed a pseudo-random number generator to select pixel locations in an image cover-

image for embedding the secret message.

Wu and Tsai, (2003), proposed a steganographic method based on pseudorandom

mechanism on gray-scale images by using pixel value differencing method where the

number of bits to be embedded in a pixel pair is decided by the difference value. The

PSNR value ranges from 40-47 dB for embedding average of 50960 bits. According

to Liang et al., (2007), pseudo-random based data embedding avoids concentration of

pixel alterations and ensures data security. They proposed a scheme that encodes the

secret bits directly into boundary pixels by checking each pixel of the cover image in

a pseudo-random order for embedding eligibility. Luo et al., (2010), proposed an edge

adaptive scheme which selects the embedding regions according to the size of secret

message and the difference between two consecutive pixels in the cover image

according to a pseudorandom number generator (PRNG). They stated that such

approach can resist both visual and statistical attacks and shows better PSNR value.

Jain et al., (2012b), presents random LSB based steganography where the edge pixels

were selected to hide the secret data. They stated that the selection of pixels for

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embedding is crucial because modified pixels in areas of the image where there are

pixels that are most like their neighbors are much more noticeable to the naked eye

and to solve this problem edge pixel were randomly selected to embed the message.

Amirtharajan et al., (2013), pro