Reversible Data Hiding in Encrypted Images Based on Adaptive Prediction and Labeling

Abstract

Recently, reversible data hiding in encrypted images (RDHEI) based on pixel prediction has been a hot topic. However, existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction, and the pixel labeling scheme is inflexible. To solve these problems, this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling. First, we design an adaptive gradient prediction (AGP), which uses eight adjacent pixels and combines four scanning methods (i.e., horizontal, vertical, diagonal, and diagonal) for prediction. AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel, which improves the prediction ability of the predictor for complex images. At the same time, we adopt an adaptive huffman coding labeling scheme, which can adaptively generate huffman codes for labeling according to different images, effectively improving the scheme's embedding performance on the dataset. The experimental results show that the algorithm has a higher embedding rate. The embedding rate on the test image Jetplane is 4.2102 bpp, and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.