In the previous multiple histograms modification (MHM) based reversible data hiding (RDH) method, the prediction-error histograms are generated by a fixed manner, which may constrain the performance owing to the lack of adaptivity. In order to compensate this, we propose a deep neural networks (DNN) based method for dynamical multiple histograms generation. Through learning the prior knowledge, DNN is able to establish the histograms with different sizes for a better redundancy exploitation. For each histogram, two optimal expansion bins will be determined to minimize the distortion caused by the modification. Besides, the strategy consisted of the memo technique and the entropy measurement are applied to accelerate the parameter optimization. Experimental results show that the proposed method outperforms some of state-of-the-art RDH methods.

在先前的基于多个直方图修改（MHM）的可逆数据隐藏（RDH）方法中，预测误差直方图是以固定方式生成的，由于缺乏适应性，可能会限制性能。为了弥补这一点，我们提出了一种基于深度神经网络（DNN）的动态多个直方图生成方法。通过学习先验知识，DNN能够建立不同大小的直方图，以更好地利用冗余。对于每个直方图，将确定两个最佳扩展箱，以最小化由修改引起的失真。此外，还采用了由备忘录技术和熵测度组成的策略来加速参数优化。实验结果表明，该方法优于一些最新的RDH方法。