Open Access

ARTICLE

An Effective Steganalysis Algorithm for Histogram-Shifting Based Reversible Data Hiding

Junxiang Wang^{1, *}, Lin Huang¹, Ying Zhang¹, Yonghong Zhu¹, Jiangqun Ni², Yunqing Shi³

1 School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen, 333403, China.
2 School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China.
3 Department of Electronics and Computer Engineering, New Jersey Institute of Technology, Newark, 07102, USA.

* Corresponding Author: Junxiang Wang. Email: .

Computers, Materials & Continua 2020, 64(1), 325-344. https://doi.org/10.32604/cmc.2020.09784

Received 18 January 2020; Accepted 10 April 2020; Issue published 20 May 2020

Abstract

To measure the security for hot searched reversible data hiding (RDH) technique, especially for the common-used histogram-shifting based RDH (denoted as HS-RDH), several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image. However, conventional steganalysis schemes focused on the previous RDH algorithms, i.e., some early spatial/pixel domain-based histogram-shifting (HS) schemes, which might cause great changes in statistical characteristics and thus be easy to be detected. For recent improved methods, such as some adaptive prediction error (PE) based embedding schemes, those conventional schemes might be invalid, since those adaptive embedding mechanism would effectively reduce the embedding trace and thus increase the difficulty of steganalysis. Therefore, a novel steganalysis method is proposed in this paper to detect recent adaptive RDH schemes and provide a more effective detection tool for RDH. The contributions of this paper could be summarized as follows. (1) By analyzing the characteristics for those adaptive HS-RDH, an effective “flat ground” based detection method is designed to fast identify whether the given image is used to hide secret data; (2) According to the empirical statistical model, double check mechanism is provided to improve the detection accuracy; (3) In addition, to further improve detection ability, some detailed information for secret data, i.e., its content and embedding location are further estimated. Compared with conventional steganalysis methods, experimental results indicate that our proposed algorithm could achieve a better detection accuracy and meanwhile acquire more detailed information on secret data.

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