  Artificial neural networks have advanced the frontiers of reversible
steganography. The core strength of neural networks is the ability to render
accurate predictions for a bewildering variety of data. Residual modulation is
recognised as the most advanced reversible steganographic algorithm for digital
images. The pivot of this algorithm is predictive analytics in which pixel
intensities are predicted given some pixel-wise contextual information. This
task can be perceived as a low-level vision problem and hence neural networks
for addressing a similar class of problems can be deployed. On top of the prior
art, this paper investigates predictability of pixel intensities based on
supervised and unsupervised learning frameworks. Predictability analysis
enables adaptive data embedding, which in turn leads to a better trade-off
between capacity and imperceptibility. While conventional methods estimate
predictability by the statistics of local image patterns, learning-based
frameworks consider further the degree to which correct predictions can be made
by a designated predictor. Not only should the image patterns be taken into
account but also the predictor in use. Experimental results show that
steganographic performance can be significantly improved by incorporating the
learning-based predictability analysers into a reversible steganographic
system.
