Optimized LSB Matching Steganography Based on Fisher Information

This paper proposes an optimized LSB matching steganography based on Fisher Information. The embedding algorithm is designed to solve the optimization problem, in which Fisher information is the objective function and embedding transferring probabilities are variables to be optimized. Fisher information is the quadratic function of the embedding transferring probabilities, and the coefficients of quadratic term are determined by the joint probability distribution of cover elements. By modeling the groups of elements in a cover image as Gaussian mixture model, the joint probability distribution of cover elements for each cover image is obtained by estimating the parameters of Gaussian mixture distribution. For each sub-Gaussian distribution in Gaussian mixture distribution, the quadratic term coefficients of Fisher information are calculated, and the optimized embedding transferring probabilities are solved by quadratic programming. By maximum posteriori probability principle, cover pixels are classified as the categories corresponding to sub-Gaussian distributions. At last, in order to embed message bits, pixels chose to add or subtract one according to the optimized transferring probabilities of the category. The experiments show that the security performance of this new algorithm is better than the existing LSB matching.