  Recent advances in deep learning have led to a paradigm shift in the field of
reversible steganography. A fundamental pillar of reversible steganography is
predictive modelling which can be realised via deep neural networks. However,
non-trivial errors exist in inferences about some out-of-distribution and noisy
data. In view of this issue, we propose to consider uncertainty in predictive
models based upon a theoretical framework of Bayesian deep learning, thereby
creating an adaptive steganographic system. Most modern deep-learning models
are regarded as deterministic because they only offer predictions while failing
to provide uncertainty measurement. Bayesian neural networks bring a
probabilistic perspective to deep learning and can be regarded as self-aware
intelligent machinery; that is, a machine that knows its own limitations. To
quantify uncertainty, we apply Bayesian statistics to model the predictive
distribution and approximate it through Monte Carlo sampling with stochastic
forward passes. We further show that predictive uncertainty can be disentangled
into aleatoric and epistemic uncertainties and these quantities can be learnt
unsupervised. Experimental results demonstrate an improvement delivered by
Bayesian uncertainty analysis upon steganographic rate-distortion performance.
