Steganalysis of LSB matching based on statistical modeling of pixel difference distributions

Abstract

This paper presents a least significant bit (LSB) matching steganography detection method based on statistical modeling of pixel difference distributions. Previous research indicates that natural images are highly correlated in a local neighborhood and that the value zero appears most frequently in intensity differences between adjacent pixels. The statistical model of the distribution of pixel difference can be established using the Laplace distribution. LSB matching steganography randomly increases or decreases the pixel value by 1 when the message is embedded; thus, the frequency of occurrence of the value zero in pixel differences changes most dramatically during message embedding. Based on the Laplacian model of pixel difference distributions, this paper proposes a method to estimate the number of the zero difference value using the number of non-zero difference values from stego-images and uses the relative estimation error between the estimated and actual values of the number of the zero difference value as the classification feature. Experimental results indicate that the proposed algorithm is effective in detecting LSB matching steganography and can achieve better detection performance than the local extreme method under most circumstances.