Multidirectional Gradient Predictor for Region-Based Reversible Data Hiding in Encrypted Images
Xuemao Zhang, Xianquan Zhang, Chunqiang Yu, Jianzhong Yang, Zhenjun Tang
Abstract
Reversible data hiding in encrypted images (RDHEIs) has attracted considerable attention, as it can facilitate the management of massive encrypted images and can be employed for covert communication. Recent research has demonstrated that the RDHEI methods with pixel prediction can achieve a more significant embedding capacity than those that do not utilize pixel prediction. Moreover, the accuracy of predictors greatly impacts the embedding capacity. Nevertheless, current predictors have several limitations, including a lack of accuracy and insufficient flexibility. To address these issues, we propose a high-precision multidirectional gradient predictor (MDGP). Based on this predictor, a novel region-based RDHEI method is proposed. Pixel prediction, image compression, data embedding, data extraction, and image recovery are conducted independently within image regions. Extensive experiments have demonstrated that the proposed MDGP predictor outperforms the current predictors in several metrics, including the average absolute errors, information entropy, and embedding capacity. The proposed RDHEI method demonstrates superior embedding capacity on the test images, and the data sets BOSSBase and BOWS2 outperforming the several state-of-the-art methods. Furthermore, it exhibits robust resilience to a variety of attacks, including perceptual attacks, statistical analysis, and patch removal attacks.