In the field of reversible data hiding (RDH), how to predict an image and embed a message into the image with smaller distortion are two important aspects. In this paper, we propose a novel and efficient RDH method by innovating an intelligent predictor and an adaptive embedding way. In the prediction stage, we first constructed a convolutional neural network (CNN) based predictor by reasonably dividing an image into four parts. In such a way, each part can be predicted by using the other three parts as the context for the improvement of the prediction performance. Compared with existing predictors, the proposed CNN predictor can use more neighboring pixels for the prediction by exploiting its multi-receptive fields and global optimization capacities. In the embedding stage, we also developed a prediction-error-ordering (PEO) based adaptive embedding strategy, which can better adapt image content and thus efficiently reduce the embedding distortion by elaborately and luminously applying background complexity to select and pair those smaller prediction errors for data hiding. With the proposed CNN prediction and embedding ways, the RDH method presented in this paper provides satisfactory results in improving the visual quality of data hidden images, e.g., the average PSNR value for the Kodak benchmark dataset can reach as high as 63.59 dB with an embedding capacity of 10,000 bits. Extensive experimental results have shown that the RDH method proposed in this paper is superior to those existing state-of-the-art works.

中文翻译：

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使用 CNN 预测和自适应嵌入进行可逆数据隐藏


在可逆数据隐藏（RDH）领域，如何预测图像以及以较小的失真将消息嵌入到图像中是两个重要方面。在本文中，我们通过创新智能预测器和自适应嵌入方式提出了一种新颖高效的 RDH 方法。在预测阶段，我们首先通过将图像合理地划分为四部分来构建基于卷积神经网络（CNN）的预测器。这样，每个部分都可以利用其他三个部分作为上下文进行预测，从而提高预测性能。与现有的预测器相比，所提出的 CNN 预测器可以利用其多感受野和全局优化能力，使用更多的相邻像素进行预测。在嵌入阶段，我们还开发了一种基于预测误差排序（PEO）的自适应嵌入策略，该策略可以更好地适应图像内容，从而通过精心且发光地应用背景复杂性来选择和配对那些较小的预测误差，从而有效地减少嵌入失真用于数据隐藏。通过所提出的 CNN 预测和嵌入方式，本文提出的 RDH 方法在提高数据隐藏图像的视觉质量方面提供了令人满意的结果，例如，通过嵌入，柯达基准数据集的平均 PSNR 值可以高达 63.59 dB容量为10,000位。大量的实验结果表明，本文提出的 RDH 方法优于现有的最先进的方法。