Progressive selection‐channel networks for image steganalysis

Abstract

Steganalysis is a detection technology against steganography that embeds secret data into digital media carriers. The selection channel, which indicates the embedding details of steganography, is well recognized in boosting the detection performance of image steganalysis. However, nearly all the selection channels are constructed in a hand-crafted manner, even when they are incorporated into end-to-end deep steganalytic networks, for which the embedding rate and steganographic algorithms also need to be predetermined. Such prior knowledge is usually assumed completely known in existing literature, which is obviously unreasonable and impractical. To address this issue, we propose to automatically learn the selection channels for deep learning-based image steganalysis in a progressive way. Specifically, we divide the image steganalysis task into two phases: selection channel estimation and steganalytic detection. For the first phase, we design a multistage progressive network, which enables the learning of selection channels in a coarse-to-fine fashion. For the second phase, we integrate the learned selection channels into the multilayers of the steganalytic network, allowing full exploitation of selection channels for accurate detection. Extensive experiments demonstrate that the proposed method can learn the selection channels rapidly and precisely, and also significantly improve the detection accuracy of the existing state-of-the-art steganographic network without any prior knowledge.