Digital Watermarking Scheme for Securing Textual Database Using Histogram Shifting Model

Information security is one of the most important methods of protecting the confidentiality and privacy of internet users.The greater the volume of data,the more the need to increase the security methods for protecting data from intruders.This task can be challenging for researchers in terms of managing enormous data and maintaining their safety and effectiveness.Protection of digital content is a major issue in maintaining the privacy and secrecy of data.Toward this end,digital watermarking is based on the concept of information security through the insertion and detection of an embedded watermark in an efficient manner.Recent methodologies in the research on digital watermarking result in data distortion after embedding the watermark.This scenario can reduce the efficiency of detecting watermarks as well as violate data confidentiality.This study adapts a robust reversible histogram shifting(HS)technique for textual data in relational databases.Furthermore,the study presents a digital watermarking scheme intended for document copyright protection and proof of ownership.The major advantage of the proposed watermarking scheme is that it can protect digital data and preserve data quality.To the best of our knowledge,this research methodology is promising for use in the HS expansion model for watermarking data on nonnumeric databases.In addition,the results showed that despite exposing the database to an insertion attacks at 50%and 75%of the watermark,the proposed algorithm can recovermore than half of the embedded watermark in all addition and alteration attacks cases.As a result,the watermark information can be retained and restored completely.

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

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.