Towards Improved Steganalysis: When Cover Selection is Used in Steganography

Abstract

This paper proposes an improved steganalytic method when cover selection is used in steganography. We observed that the covers selected by existing cover selection methods normally have different characteristics from normal ones, and propose a steganalytic method to capture such differences. As a result, the detection accuracy of steganalysis is increased. In our method, we consider a number of images collected from one or more target (suspected but not known) users, and use an unsupervised learning algorithm such as $k$-means to adapt the performance of a pre-trained classifier towards the cover selection operation of the target user(s). The adaptation is done via pseudo-labels from the suspected images themselves, thus allowing the re-trained classifier more aligned with the cover selection operation of the target user(s). We give experimental results to show that our method can indeed help increase the detection accuracy, especially when the percentage of stego images is between 0.3 and 0.7.