Universal image steganalysis using rate-distortion curves

The goal of image steganography is to embed information in a cover image using modifications that are undetectable. In actual practice, however, most techniques produce stego images that are perceptually identical to the cover images but exhibit statistical irregularities that distinguish them from cover images. Statistical steganalysis exploits these irregularities in order to provide the best discrimination between cover and stego images. In general, the process utilizes a heuristically chosen feature set along with a classifier trained on suitable data sets. In this paper, we propose an alternative feature set for steganalysis based on rate-distortion characteristics of images. Our features are based on two key observations: i) data hiding methods typically increase the image entropy in order to encode hidden messages; ii) data hiding methods are limited to the set of small, imperceptible distortions. The proposed feature set is used as the basis of a steganalysis algorithm and its performance is investigated using different data hiding methods.