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.

翻译：人工神经网络推动了可逆隐写术的前沿发展。神经网络的核心优势在于能够对多种多样的数据做出精确预测。残差调制被认为是目前最先进的数字图像可逆隐写算法。该算法的关键在于预测分析，即根据像素级的上下文信息预测像素强度。这一任务可被视作低层视觉问题，因此处理同类问题的神经网络模型可被部署应用。在现有研究基础上，本文探究了基于监督学习和无监督学习框架的像素强度可预测性。可预测性分析能够实现自适应数据嵌入，进而提升容量与隐蔽性之间的平衡。传统方法通过局部图像模式的统计特征估算可预测性，而基于学习的框架进一步考虑指定预测器能够做出正确预测的程度——不仅需要考虑图像模式，还需考虑所使用的预测器。实验结果表明，将基于学习的可预测性分析器集成至可逆隐写系统可显著提升隐写性能。