oday the growth in the information technology, especially in computer networks such as Internet, Mobile communication, and Digital Multimedia applications such as Digital camera, handset video etc. has opened new opportunities in scientific and commercial applications. But this progress has also led to many serious problems such as hacking, duplications and malevolent usage of digital information. Steganography finds its role in attempt to address these growing concerns [6]. Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. In image steganography, the aim is to hide information in to a given image called as cover image and the diagnosis of the hidden information will be probably difficult [10]. Every steganographic methods consist of a cover image and a stego image.

Many approaches of information hiding have been proposed for different applications, such as copyright protection, secret transmission, tampering detection, and image authentication.

The most well-known data hiding scheme is the least significant bits (LSBs) substitution method [1], [13]. This method embeds fixed-length secret bits into the least significant bits of pixels by directly replacing the LSBs of cover image with the secret message bits. Although this method is simple, it generally effects noticeable distortion when the number of embedded bits for each pixel exceeds three [1]. Several methods have been proposed to reduce the distortion induced by LSBs substitution. OPAP scheme searches the minimal distortion value which LSBs equal the embedded bits and replaces stego-pixel value with it [12]. Another way of improving LSBs scheme is to reduce the amount of alterations necessary to be introduced into the cover image for data hiding when the number of secret bits is significantly less than that of available cover pixels.

Another method called optimal pixel adjustment process (OPAP) method [14] is introduced to reduce the distortion caused by LSB replacement. In LSB and OPAP methods one pixel is used as an embedding unit [11], and conceal data into the right-most LSBs. OPAP is conceptually defined as matching pixel to its optimal level. OPAP effectively reduces the image distortion compared with the traditional LSB method [12]. But in OPAP method, imbalanced embedding distortion emerges and is vulnerable to steganalysis. LSB and OPAP methods are not suitable for applications requiring high payload.

An efficient data hiding method is proposed for gray-scale images by utilizing the diamond encoding concept (DE). We first transform the secret data into a sequence of digits, and the cover image is partitioned into non-overlapping blocks of two consecutive pixels. The diamond encoding method produces a diamond characteristic value (DCV) of the pixel-pair block, and the DCV [3] is revised as the embedded secret digit after data embedding procedure. For each block, the diamond encoding technique addresses the minimal changes of two pixel values under the embedding parameter k. In other words, the difference between the cover-block and the stego-block is never more than k, and the embedding capacity of a block equals

system cannot be arbitrarily selected. For example, when is 1, 2, and 3, then digits in a 5-ary, 13-ary, and 25-ary notational system are used to embed data, respectively. However, embedding digits in a 4-ary (i.e., 1 bit per pixel) or 16-ary (i.e., 2 bits per pixel) notational system are not supported in DE. Secondly, ) , ( y x ? in DE [1], [2], [4] is defined by a diamond shape, which may lead to some unnecessary distortion when k > 2. In fact, there exists a better

, where f is the extraction function and S B is the message digit in B-ary notational system to be concealed [1], [3]. Data embedding is done by replacing

Suppose the cover image is of size M×M, Sis the message bits to be concealed and the size of S is |S|. First we calculate the minimum B such that all the message bits can be embedded. Then, message digits are sequentially concealed into pairs of pixels. First

As APPM is proved to offer better security against detection and lower distortion, we can take forward APPM for hiding text in an image. This method is proposed to explore a better mechanism and provide better security and lower distortion for embedding text in images. The ascii values of all the characters in the given text are converted to binary values and then they are partitioned into groups of bits. Then the embedding process is performed for embedding them into an image.

Data embedding is done by replacing ) , ( y x with ) , ( y x ? ?

. These are the reference coordinate and pixel value from the neighborhood set. The concept of a PPM-based steganographic method is that, let B S be the message bit is to be concealed and the range of B S is between 0 and 1 ? B . And there should be a coordinate ) , ( y x ? ? has to be found such that

. That is why the range of ii. Finding Neighborhood Set And Extraction Function In this module, the extraction function is explained. By using this method, we can get a simple extraction function and compact neighborhood set. Thus the proposed method enhances the embedding efficiency. The quality of image obtained by this method is much better than the other existing data hiding method such as OPAP and DE [1]. Another two advantages of this proposed method are higher payload capability and less detectability. Compact notional

Here the secret data has to be embedded into the given cover image. For this first we should calculate the image size and message size. If the message size exceeds size of the image, then the embedding procedure cannot be done. Consider the image size as M*M, For S message bits the size of secret message S is |S|. By using these, calculate the minimum B value such that all the message bits can be embedded. The message digits will be sequentially concealed into pairs of pixels.

The data embedding process is shown by a flowchart in fig 3

When data embed in an image, the pixel values in that image may modified and this process is known as image distortion or embedding distortion. MSE (Mean Square Error) is used to measure this distortion. MSE is calculated by the following equation

Where M×M is the image size, p i,j denotes the pixel values of original image and p? i,j denotes the pixel values of stego image. Here the mean square error between the cover image and stego image is represented by MSE. The smaller MSE is for APPM which indicate the better image quality. APPM is flexible and gives less mean square error while embedding digits [1] and as well as embedding text also.

This paper proposed an efficient data embedding algorithm for hiding text in an image based on APPM. Here two pixel positions are scanned and are considered as a scanning unit. And a specially designed neighborhood set with smallest notational system is used for embedding text and hence a better image quality is achieved. The steganalysis results of stego images are similar to those of the cover images, which offer a secure communication under adjustable embedding capacity. It also contains additional features such as digital watermark and encryption of secret messages for the provision of more security. APPM technique can also be used for embedding data in audio and video also. All these various features made this APPM technique a straightforward and economical embedding method for the data hiding.