Chi-square-based steganalysis method against modified pixel-value differencing steganography

Abstract

Pixel-value differencing (PVD) steganography can embed a large quantity of information and effectively resist regular/singular (RS) detection. Recent steganography techniques that are based on PVD have only focused on improving embedding capacity and image quality. In terms of security, PVD steganography has typically only been tested against well-known RS detection analysis or pixel difference histogram attacks. Modified PVD (MPVD) is an improved method that eliminates certain detectable features of PVD, thereby increasing security. State-of-the-art studies in steganalysis have attempted to detect MPVD steganography by using features of the differences between cover images and stego images, and they have generated models to test suspicious images. However, these methods have low accuracy when the embedding capacity is low. In this study, we propose a chi-square goodness-of-fit-based steganalysis technique that can be applied to MPVD steganography. Experimental results demonstrate that the method effectively detects MPVD steganography even when the embedding ratio is low. Furthermore, the accuracy, precision, and implementation of the method are better than those of existing state-of-the-art techniques in detecting data hidden using MPVD steganography. Therefore, the proposed method contributes to the security analysis of PVD-extended steganography.