Binary image steganalysis based on local texture pattern

In this paper, we propose a novel steganalytic scheme based on local texture pattern (LTP) to detect binary image steganography. We first assess how the expanded LTPs capture embedding distortions exactly. Considering curse of dimensionality when expanding LTPs, we employ Manhattan distance to measure the pixels correlation in a 5×5 sized block and select the pixels with closely correlation to remove some LTPs that are not interested. Although the stego image can maintain good visual quality, steganography scheme changes the inter-pixels correlation of binary image. Therefore we utilize totally 8192 LTPs histogram to define a 8192-dimensional steganalytic feature set. Original images and stego images are classified by ensemble classifier. Experimental results show that the proposed steganalytic method can more effectively detect state-of-the-art binary image steganography schemes compared with other steganalytic schemes.