On improving steganalysis against cover selection steganography

Abstract There are many cover selection methods currently in use within steganography to ensure that secret data embedded in digital images is hard to detect. This paper proposes an improved steganalytic method to enhance the detection accuracy on steganography in digital images by considering image texture complexity. We propose a measurement of image texture complexity based on gray level co-occurrence matrix-based (GLCM) multi-scale fusion, which is integrated into the classifier’s detection process. Our method refines the traditional classification process by exponentiating the probability of an image being voted “clear” with its calculated complexity value and comparing this value to the probability of it being voted “stego”. Images with higher texture complexity are more likely to be identified as containing secret data. Experimental results show that our method can effectively increase detection accuracy of steganalysis when cover selection is employed in steganography.