An effective steganalysis algorithm for histogram-shifting based reversible data hiding

Document Type

Article

Publication Date

1-1-2020

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.

Identifier

85090898488 (Scopus)

Publication Title

Computers Materials and Continua

External Full Text Location

https://doi.org/10.32604/CMC.2020.09784

e-ISSN

15462226

ISSN

15462218

First Page

325

Last Page

344

Issue

1

Volume

64

Grant

61563022

Fund Ref

National Natural Science Foundation of China

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