Message estimation for universal steganalysis using multi-classification support vector machine

Abstract

To prevent misusing of the steganography from the terrorists, effective steganalysis schemes which discriminate the stego-images from suspicious images are necessary. Some steganalysis methods can accurately estimate the length of embedded messages but they are only useful in the pre-defined condition. Active steganalysis methods are powerful in length estimation such as regular singular (RS) and sample pairs analysis (SPA) steganalysis schemes, but they would become invalid in frequency domain. Passive steganalysis methods may discriminate stego-images from suspicious images in spatial and frequency domains such as Lyu and Fraid's steganalysis scheme, but they could not estimate the length of hidden messages. Although length estimation has been discussed in the active steganalysis methods for a while, it is a novel study in passive steganalysis method. We improve the Lyu and Fraid's universal steganalysis scheme and design an efficient length estimation policy in passive steganalysis methods. Experimental results demonstrate the efficiency and practicability of the proposed universal steganalysis scheme.