The goal of quantitative steganalysis is to provide an estimate of the size of the embedded message once an image has been detected as containing secret data. For steganographic algorithms free of serious design flaws, such as schemes based on least significant bit replacement, the most competitive quantitative detectors have traditionally been built as regressors in rich media models. Considering the recent advances in binary steganalysis due to deep learning, in this paper we use the features extracted from the activation of such CNN detectors for the task of payload estimation. The merit of the proposed architecture is demonstrated experimentally on steganographic algorithms operating both in the spatial and JPEG domain.
Mo Chen, Mehdi Boroumand, Jessica Fridrich, "Deep Learning Regressors for Quantitative Steganalysis" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics, 2018, pp 160-1 - 160-7, https://doi.org/10.2352/ISSN.2470-1173.2018.07.MWSF-160