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Reversible data hiding (RDH) has unique advantage in copyright and integrity protection for multimedia contents. As a typical RDH scheme, histogram shifting technique (HS) has found wide applications due to its high quality of marked image. At present, most existing HS-based RDH schemes rely on single histogram generated from cover image to hide data. Since the single histogram-based approach (SH_RDH) commonly employs smooth regions in the cover image for data hiding, it might not well utilize the cover image and exploit the correlations among image contents of different texture characteristics. In this paper, a novel RDH general framework using multiple histograms modification (MH_RDH) is proposed, which involves two key issues as follows: 1) the construction of multiple histograms based on optimized multi-features and 2) the rate allocation among multiple histograms is formulated as the one of rate-distortion optimization and solved with evolutionary algorithms. The experimental results show that the proposed method could considerably increase the payload of current MH_RDH-based embedding (ranging from 0.2 to 0.7 bpp for most test images) and outperform the other state-of-the-art SH_RDH and MH_RDH schemes. [ABSTRACT FROM AUTHOR]

Highlights • Employ FCM clustering to build multiple closely correlated carriers for MH_RDH. • Attempt to apply machine learning algorithm to reversible data hiding applications. • Effectively represent each pixel with a set of features, which will be used in FCM. • Optimize the hyper parameters in clustering process for better performance in MH_RDH. • Experimental results show the superiority of our FCM based MH_RDH. Abstract Reversible data hiding algorithm (RDH) has been widely used in multimedia's copyright protection and content integrity authentication. As a typical RDH scheme, histogram shifting (HS) is extensively investigated due to its high quality of stego-image. Most existing HS based RDH schemes utilize prediction and sorting techniques to build single sharp histogram, which exploit the smooth areas in cover image for data hiding. To take advantages of the correlation among image contents of different texture characteristics, several multiple histograms based RDHs (MH_RDH) are proposed recently, which resort on some rigid rules, e.g. single feature based sorting followed by uniform segmentation of sorted sequence, to construct the multiple histograms. In this paper, the clustering algorithm, i.e. Fuzzy C-means (FCM) clustering, is introduced for the construction of multiple histograms. The FCM equipped with deliberately designed features is employed to classify the cover carriers, e.g. prediction errors, into different clusters with similar traits, which are then used to build the multiple histograms for efficient data embedding. Experimental results demonstrate the superior performance of the proposed scheme over other state-of-the-art ones. [ABSTRACT FROM AUTHOR]

• The ridge regression predictor is utilized to minimize the sum of squared residuals between the prediction pixels and the target pixels for reducing prediction error. • The ridge regression predictor only decreases the weights of abnormal samples and weakens the influence of abnormal samples on prediction, avoiding the overfitting phenomenon. • the proposed method also selects the nearest neighbouring pixel around the target pixels as the training sets and the supported sets of ridge regression predictor, which further improves the prediction accuracy. An effective error prediction algorithm is the key to improving the embedding performance of reversible data hiding schemes. In this paper, high-performance ridge regression predictor-based reversible data hiding (RDH) is proposed. The ridge regression predictor is an adaptive predictor that adds L2 regularisation to minimise the residual sum of squares between the prediction pixels and the target pixels. The L2 norm as a penalty function decreases the weights for the prediction coefficients of unimportant pixels. In other words, the ridge regression predictor limits prediction coefficients that have negative or no influence on predicting the target pixels (abnormal samples). The ridge regression predictor allows the prediction coefficients to be small, which avoids the overfitting problem and enhances tamper-resistance and generalisation ability. In addition, to increase the prediction accuracy of the ridge regression predictor, the proposed method employs small samples to obtain more accurate prediction values. The neighbouring pixels closest to the target pixels are selected as the training sets and supported sets during the prediction process. In summary, the ridge regression predictor can generate an error plane that is more suitable for embedding, thereby improving the embedding performance of RDH. Extensive experimental results also show that the proposed method is superior to the state-of-the-art RDH schemes in terms of prediction accuracy and embedding performance. [ABSTRACT FROM AUTHOR]