Convolutional Neural Network-driven Optimal Prediction for Image Reversible Data Hiding
Xiaoya Zhang, Yuanzhi Yao, Nenghai Yu · 2021
Reversible data hiding aims to embed data into cover digital media in a reversible way. The key issue of image reversible data hiding is to construct the sharply distributed prediction error histogram using advanced pixel prediction. Inspired by the progress of image super-resolution exploiting convolutional neural networks (CNN), CNN predictors can improve the prediction accuracy compared with conventional predictors generally. However, CNN predictors fail to achieve the best prediction accuracy in some cases due to the dependence on training data. To remedy this drawback, the CNN-driven optimal prediction for image reversible data hiding is proposed in this paper. Instead of only utilizing one specific predictor for prediction error histogram construction, the optimal prediction mechanism is designed by incorporating CNN predictors and conventional predictors. Extensive experiments demonstrate the merits of the proposed method in terms of prediction accuracy and marked image quality.