Secure Binary Image Steganography Based on Minimizing the Distortion on the Texture

Abstract

Most state-of-the-art binary image steganographic techniques only consider the flipping distortion according to the human visual system, which will be not secure when they are attacked by steganalyzers. In this paper, a binary image steganographic scheme that aims to minimize the embedding distortion on the texture is presented. We extract the complement, rotation, and mirroring-invariant local texture patterns (crmiLTPs) from the binary image first. The weighted sum of crmiLTP changes when flipping one pixel is then employed to measure the flipping distortion corresponding to that pixel. By testing on both simple binary images and the constructed image data set, we show that the proposed measurement can well describe the distortions on both visual quality and statistics. Based on the proposed measurement, a practical steganographic scheme is developed. The steganographic scheme generates the cover vector by dividing the scrambled image into superpixels. Thereafter, the syndrome-trellis code is employed to minimize the designed embedding distortion. Experimental results have demonstrated that the proposed steganographic scheme can achieve statistical security without degrading the image quality or the embedding capacity.