How to predict images is an important issue in the reversible data hiding (RDH) community. In this letter, we propose a novel CNN-based prediction approach by luminously dividing a grayscale image into two sets and applying one set to predict the other set for data embedding. The proposed CNN predictor is a lightweight and computation-efficient network with the capabilities of multi receptive fields and global optimization. This CNN predictor can be trained quickly and well by using 1000 images randomly selected from ImageNet. Furthermore, we propose a two stages of embedding scheme for this predictor. Experimental results show that the CNN predictor can make full use of more surrounding pixels to promote the prediction performance. Furthermore, in the experimental way we have shown that the CNN predictor with expansion embedding and histogram shifting techniques can provide better embedding performance in comparison with those classical linear predictors.

中文翻译：

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基于CNN预测的可逆数据隐藏

如何预测图像是可逆数据隐藏（RDH）社区中的重要问题。在这封信中，我们提出了一种新颖的基于CNN的预测方法，该方法是将灰度图像分为两组，然后应用一组预测另一组进行数据嵌入。提出的CNN预测器是一种轻量级且计算效率高的网络，具有多接收域和全局优化的功能。通过使用从ImageNet随机选择的1000张图像，可以快速，良好地训练该CNN预测器。此外，我们为该预测器提出了两个阶段的嵌入方案。实验结果表明，CNN预测器可以充分利用周围的更多像素，从而提高预测性能。此外，