Prediction-error expansion (PEE) is the most successful reversible data hiding (RDH) technique, and existing PEE-based RDH methods are mainly based on the modification of one-or two-dimensional prediction-error histogram (PEH). The two-dimensional PEH-based methods perform generally better than those based on onedimensional PEH; however, their performance is still unsatisfactory since the PEH modification manner is fixed and independent of image content. In this paper, we propose a new RDH method based on PEE for multiple histograms. Unlike the previous methods, we consider in this paper a sequence of histograms and devise a new embedding mechanism based on multiple histograms modification (MHM). A complexity measurement is computed for each pixel according to its context, and the pixels with a given complexity are collected together to generate a PEH. By varying the complexity to cover the whole image, a sequence of histograms can be generated. Then, two expansion bins are selected in each generated histogram and data embedding is realized based on MHM. Here, the expansion bins are adaptively selected considering the image content such that the embedding distortion is minimized. With such selected expansion bins, the proposed MHM-based RDH method works well. Experimental results show that the proposed method outperforms the conventional PEE and its miscellaneous extensions including both one-or two-dimensional PEH-based ones.Index Terms-Reversible data hiding, prediction-error expansion, multiple histograms modification, adaptive embedding.

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The steganalysis of least significant bit (LSB) matching steganography has been well realized by many highly sensitive detectors so far, but few of them can analyze further to higher levels. Most of current steganalysis methods still stay in the low-level stage that can only detect the existence of secret message. In this paper, we focus on higher level steganalysis and an improved method for payload location of LSB matching is proposed. Our work is based on the combination and improvement of previous techniques including message length estimation and payload location. The proposed method first solves a regression problem to estimate the embedding rate utilizing steganalysis features. Then, the embedding payload can be located via optimal cover estimation and improved residuals calculation, in which the embedding rate achieved above is a crucial parameter. By this approach, the payload location method can be applied to practical use needless of any prior knowledge. Experimental results show that the proposed method outperforms the state-of-the-art work with a higher accuracy for payload location of LSB matching.

In this paper, we propose an efficient data hiding scheme based on reference pixel and block selection to further improve the embedding performance of histogram shifting. Specifically, we first divide the original image into non-overlapping blocks of an adjustable size. Then for each block, we assign the median of pixels as the reference pixel and the number of pixels equal to the reference value as the smooth level. In this way, difference histograms for each smooth level can be constructed. We embed the secret data using histogram shifting from the highest level histogram to lower level ones instead of sequential embedding. By this means, our proposed reversible data hiding scheme can adaptively embed data in the smooth blocks and thus improve the marked image quality with a comparable embedding capacity. The experimental results also demonstrate its superiority over some state-of-the-art reversible data hiding works.