A Texture-Aware CNN Predictor for Reversible Data Hiding

Reversible data hiding (RDH) enables the reversible embedding of additional data into cover media, allowing the cover media to be perfectly recovered after extracting the embedded data. RDH relies on accurate prediction of pixels to generate sharply distributed prediction error histograms, thereby maximizing embedding capacity and minimizing visual distortion. While convolutional neural network (CNN)-based predictors excel in smooth regions of cover images by leveraging local correlation, they often fail to produce accurate predictions in the textured regions. To address the limitation of CNN predictors, we propose a novel attention fusion-based CNN predictor (AFCNNP) that adaptively combines the CNN predictor with a non-local means (NLM) predictor. The proposed fusion framework learns spatial weight maps to favor CNN predictions in smooth regions and NLM predictions in textured regions. The experimental results show that the proposed framework outperforms other state-of-the-art CNN predictors by significantly lowering the mean absolute error, mean squared error, and variance of prediction errors, leading to more accurate pixel predictions. With the proposed fusion framework, the embedding and visual performance of prediction error expansion (PEE)-based RDH is improved compared to typical CNN-based RDH methods.