An efficient data embedding scheme for digital images based on particle swarm optimization with LSBMR

Abstract

As an important component of multimedia information security, information hiding has received wide attention in recent years. In fact, intellectual properties are becoming harder to protect and so are original contents, that's why we need techniques to be developed such as Image Steganography. Steganography is a technique for information hiding. It aims to embed secret data in to digital cover media, such as Images, Audio and Video without being suspicious. Evolutionary algorithms are stochastic search methods that mimic the natural bio logical evolution and the social behaviour of species. Such algorithms have been developed to arrive at near optimum solutions to large-scale optimization problems. For which traditional mathematical techniques may fail. In this paper, a novel stenographic method, based on Particle Swarm Optimization algorithm (PSO) is proposed, PSO is an evolutionary computational model based on Swarm intelligence. Kennedy and Elbe hart developed PSO through simulating social behaviour. In PSO, each individual is called a “particle” and the position of each particle is a candidate solution to a problem. LSB Matching Revisited (LSBMR) image steganography using Particle Swarm Optimization algorithm (PSO) is proposed, in Particle Swarm Optimization algorithm (PSO) is used to select the embedding regions according to the size of the secret message and to optimize the threshold value of the selected image regions. In order to improve the quality of stego images, an optimal substitution matrix for transforming the secret messages is first derived by means of the PSO algorithm. The experimental results show that our proposed method has larger message capacity and better image quality then the existing method.