Analysis of the Scalability of a Deep-Learning Network for Steganography “Into the Wild”

Since the emergence of deep learning and its adoption in steganalysis fields, most of the reference articles kept using small to medium size CNN, and learn them on relatively small databases. In this paper, after a solid discussion related to the observed behaviour of CNNs as a function of their sizes and the database size, we confirm that the error’s power law also stands in steganalysis, and this in a border case, i.e. with a medium-size network, on a big, constrained and very diverse database.