Image Steganography Using Penalty Function Method and PSO

Abstract

This paper presents an innovative methodology in the realm of Pixel Value Differencing with Modulus Function (PVDMF) steganography. It is modelled as an optimization problem to minimize the mean square error between the cover and stego image. A novel approach, Penalty Function Based Particle Swarm Optimization Algorithm (PFBPSOA), is proposed for the optimization. PFBPSOA addresses the optimization problem with constraints by converting it into an unconstrained optimization problem through the application of the penalty function methodology. In this method, for each constraint, a weight term is added to the objective function to prevent constraint violation. Experiment results show that the proposed method preserves good image metrics such as hiding capacity, Peak Signal to Noise Ratio (PSNR), and Quality Index (QI). The proposed methodology is immune to pixel value difference histogram (PDH) analysis.