CNN Prediction Based Reversible Data Hiding
Runwen Hu, Shijun Xiang
Abstract
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.