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.

翻译：深度学习的最新进展引发了可逆隐写术领域的范式转变。可逆隐写术的一个基础支柱是预测建模，这可以通过深度神经网络实现。然而，在对某些分布外数据和噪声数据进行推断时，存在显著误差。针对这一问题，我们提出基于贝叶斯深度学习的理论框架来考虑预测模型中的不确定性，从而构建一个自适应隐写系统。大多数现代深度学习模型被视为确定性模型，因为它们仅提供预测而无法给出不确定性度量。贝叶斯神经网络为深度学习带来了概率视角，可视为具有自我意识的智能机器，即能够认知自身局限性的机器。为量化不确定性，我们应用贝叶斯统计对预测分布进行建模，并通过带随机前向传播的蒙特卡洛采样进行近似。我们进一步证明预测不确定性可分解为偶然不确定性和认知不确定性，且这些量可通过无监督学习方式获取。实验结果表明，贝叶斯不确定性分析在隐写率失真性能上带来了显著提升。