IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 9, Issue 2, Ver. II (Mar - Apr. 2014), PP 41-49 www.iosrjournals.org www.iosrjournals.org 41 | Page Implementation of Segment Compression Steganographic Algorithm 1 Shilpa S. Gaikwad, 2 Maruti B. Zalte 1,2 Department of Electronics and Telecommunication K.J.Somaiya College of Engineering Mumbai-77, India Abstract: Steganography is the technique of hiding confidential information within any media. Using steganography, information can be hidden in different embedding mediums, known as carriers. These carriers can be images, audio files, video files, and text files. The focus in this paper is on the use of an image file as a carrier, a new steganographic technique for concealing digital images: the Segment Compression Steganographic Algorithm (SCSA) which is based on the Karhunen-Loève Transform (KLT) is presented. A detailed presentation of the component parts of the algorithm follows, accompanied by quantitative analyses of parameters of interest. In addition, we make a few suggestions regarding possible further refinements of the SCSA. Index Terms: Steganography, Least Significant Bit, Secret Information I. INTRODUCTION In Segment Compression Steganographic Algorithm the input data are first compressed using the KLT in order to achieve a higher concealing capacity, and then hidden in the least significant bits of the carrier object, which is represented in the RGB spatial domain. By combining the two procedures, we are aiming at three different research directions: increasing the capacity for concealing large messages, attaining a high quality stego object so that it is almost imperceptibly different from the carrier object and improving the execution time of the algorithm’s implementation by concurrently processing different image segments (blocks) on a multi -core microprocessor. The final purpose for creating this algorithm is to implement it on yet to be released multi-core architecture mobile devices (specifically mobile phones). The Karhunen-Loève Transform, also known as the Hotelling Transform or Eigenvector Transform allows an optimal compression, superior for instance to the one achieved by the popular Discrete Cosine Transform (DCT), the latter being in fact just an approximation of the KLT [3].The KLT completely decorrelates the input signals and is able to reallocate their energy in just a few components. KLT’s greatest disadvantage with respect to other linear transforms is that it requires a great amount of processing on sometimes large sets of data. Because of this, practical implementations of the KLT algorithm require important computational resources and are lengthy in terms of execution time [1]. We plan to overcome this disadvantage by dividing a digital image into blocks (segments), thereby significantly reducing the time costs. II. QUALITY METRICS OF STEGANOGRAHIC ALGORITHM In order to evaluate the performance of Segment Compression Steganographic Algorithm, we took different parameters like compression rate, hiding time, recovery time, carrier error, message error, amount of data which can be embedded in carrier, etc. The steganography algorithm alters the carrier image by embedding information pertaining to the secret message. We can calculate the difference (alteration rate) between the original carrier image (C) and the processed image, which we will henceforth call the stego image (S). This value is the Carrier Error. Also, because of the KLT compression and of subsequent processing, the message recovered (R) from the stego image will not be identical to the original hidden message (M). The difference between M and R is the Message Error. We find that the message error increases with segment size, while the carrier error decreases. Thus, we must make a compromise when it comes to choosing the segment size. If we want a carrier alteration as imperceptible as possible, than a larger segment size is indicated, but if we are more interested in the quality of the recovered message, than we should aim for a smaller segment size. III. SCSA – step by step description The Segment Compression Steganographic Algorithm, like any other steganographic algorithm, is composed of two perfectly mirrored parts: obtaining the steganographied image (stego), which takes place at the sender level, and recovering the payload, which takes place at the receiver level.