Using genetic algorithm based model in text steganography

Abstract

Steganography is the art of hiding information in a cover medium in such a way that the existence of any communication itself is undetectable. It can be applied in open systems such as the internet. There exist a number of steganography tools for embedding secret messages in several cover medium, but the most important property of a cover medium is the amount of data that can be stored inside it, without changing the noticeable properties of the cover, which in this case genetic algorithm approach allows variation in text length. Consequently, there is an increase in sophisticated techniques with which to analyze and recover that information. The cover medium used includes image, audio, video and text. The available text Steganography techniques include format-based method, random and statistical character generation and linguistic method. In this project we present a Genetic Algorithm approach text stenography aimed at increasing robustness and capacity of hidden data. The cover text used is a set of random numbers. First, the secret text/payload is encrypted and then converted into its ASCII form. Genetic algorithm is then applied on the cover text obtained from a set of randomly generated numbers to embed the secret message (ASCII form) into the text data (random numbers). The cover text generated is dependent on the length of the secret message. Once optimal results have been reached the embedding process begins to produce a stego text. An extraction algorithm is applied to get the original secret message. The results show that the proposed approach satisfies security, robustness and hiding capacity requirements