International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 10 No: 06 30 100306-7474 IJBAS-IJENS © December 2010 IJENS I J E N S Improving Embedding Capacity with Minimum Degradation of Stego-image Mahwish Bano, Tasneem M. Shah, and Shaheryar Malik used to embed message, which has high risk of delectability Abstract—Embedding maximum information in a stego-image with minimum change in its appearance has been a major concern in image-based steganography techniques. In this paper, we present a strategy of attaining maximum embedding capacity in an image in a way that maximum possible neighboring pixels are analyzed for their frequencies, to determine the amount of information to be added in each pixel. The technique provides a seamless insertion of data into the carrier image and reduces the error assessment and artifacts insertion required to a minimal. We justify our approach with the help of an experimental evaluation on a prototypic implementation of the proposed model. Keywords—Steganography, Least Significant Bit Insertion, Security and Cryptography I. INTRODUCTION S TEGANOGRAPHY is an art of transferring message in a way that the existence of message is concealed. Steganography can utilize various medium as carriers of the message. These mediums may include the classical methods of steganography using text, like character marking, invisible ink, using pin pictures, type-writer correction), images, and audio, video signals. Most of the steganography techniques use images a stego-medium. Information can be hidden in images through many different ways. The most common approaches to information hiding in images are: Least significant bit (LSB) insertion [5], Masking and filtering techniques [3], Algorithms and transformations [3]. Masking and filtering techniques hide information by marking an image in a manner similar to paper watermarks [2]. Because watermarking techniques are more integrated into the image, they may be applied without fear of image destruction from lossy compression. The least significant bit insertion (LSB) is the most widely used image steganography technique [2]. It embeds message in the least-significant bits of each pixel. In order to increase the embedding capacity, two or more bits in each pixel can be . T.M. Shah,Professor/Chair Department of Mathematics, Air University,Islamabad,Pakistan(email:dr.tasneem@mail.au.edu.pk ) Mahwish Bano, Assistant professor, Air University, Islamabad, Pakistan (email: mahwish@mail.au.edu.pk) S. Malik, Assistant professor,Air University,Islamabad, Pakistan(email:malikshary@yahoo.com) and image degradation [8]. The LSB techniques might use a fixed least significant bit insertion scheme, in which the bits of data added in each pixel remains constant, or a variable least significant bit insertion, in which the number of bits added in each pixel vary on the surrounding pixels, to avoid degrading the image fidelity In this paper we discuss the embedding of text into image through variable size least significant bit insertion. The process of insertion of text in our proposed approach is not sequential, rather it follows a random order, based on a random algorithm. The technique proposed aims at providing not only maximum insertion capacity, but also performs a maximum analysis of surrounding pixels to determine the embedding capacity of each pixel. The process results in a stego-image which is very much similar in appearance to the original image. The rest of the paper is organized as follows: Section II discusses the proposed steganography model and its different stages, Section III presents an experimental evaluation of the proposed steganography model, and Section IV discusses the conclusion and future work. II. PROPOSED STEGANOGRAPHY MODEL In this section, we propose a steganography model that ensures maximum embedding of information in both gray scalale and colored images, and also ensures that maximum pixels are analyzed to determine the embedding capacity. This would lead to a reduction of the overall error induction in the image. The stego-image obtained after application of this analysis would not only have maximum amount of information, but would also have the minimum difference in appearance with the original image. Fig 1 shows a block diagram of the proposed steganography model. The approach used in the model requires a random/pseudo-random number generation algorithm, and a transposition algorithm. For simplicity, we use one of the simplest algorithms for random number generation to explain our model. In practice the model can utilize any of the other well-accepted random generation algorithms [4][10], depending on the nature of the message and security requirements. Discussion of different steps involved in model is: