Effect of Imprecise Knowledge of the Selection Channel on Steganalysis

It has recently been shown that steganalysis of content-adaptive steganography can be improved when the Warden incorporates in her detector the knowledge of the selection channel -- the probabilities with which the individual cover elements were modified during embedding. Such attacks implicitly assume that the Warden knows at least approximately the payload size. In this paper, we study the loss of detection accuracy when the Warden uses a selection channel that was imprecisely determined either due to lack of information or the stego changes themselves. The loss is investigated for two types of qualitatively different detectors -- binary classifiers equipped with selection-channel-aware rich models and optimal detectors derived using the theory of hypothesis testing from a cover model. Two different embedding paradigms are addressed -- steganography based on minimizing distortion and embedding that minimizes the detectability of an optimal detector within a chosen cover model. Remarkably, the experimental and theoretical evidence are qualitatively in agreement across different embedding methods, and both point out that inaccuracies in the selection channel do not have a strong effect on steganalysis detection errors. It pays off to use imprecise selection channel rather than none. Our findings validate the use of selection-channel-aware detectors in practice.