Reference Channels for Steganalysis of Images with Convolutional Neural Networks
Mo Chen, Mehdi Boroumand, Jessica Fridrich · 2019
When available, reference signals may dramatically improve the accuracy of steganalysis. Particularly powerful reference signals are embedding invariants that exist when the steganographic algorithm swaps values from small disjoint subsets of the cover elements' dynamic range, such as, but not limited to, embedding schemes utilizing least significant bit replacement. This paper describes a general method how to prepare such reference signals for a certain type of embedding operations, and incorporate them in detectors built as convolutional networks to improve their detection accuracy. The beneficial effect of reference signals is shown experimentally in both the spatial and especially JPEG domain, on model-based steganography and a generic LSB flipper with and without stochastic restoration of the histogram (OutGuess).