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 protectin...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.展开更多
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 ...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.展开更多
为解决可逆信息隐藏(reversible data hiding,RDH)容量受限的问题,提出了一种基于相邻均值差的可逆信息隐藏(neighboring mean difference reversible data hiding,NMDRDH)算法。相邻均值差(neighboring mean diffe-rence,NMD):计算两...为解决可逆信息隐藏(reversible data hiding,RDH)容量受限的问题,提出了一种基于相邻均值差的可逆信息隐藏(neighboring mean difference reversible data hiding,NMDRDH)算法。相邻均值差(neighboring mean diffe-rence,NMD):计算两个相邻数值的平均值与其中一个数值的差值。NMD将差值最小化,使数据更加集中。首先将图像进行分块,然后在分块上应用NMD生成差值直方图,最后通过平移差值直方图,利用峰值点来嵌入秘密信息。由于NMD使生成的差值直方图具有更多的峰值点,所以该方法可嵌入更多的秘密信息。实验结果表明,采用本算法,原始图像恢复率和秘密信息提取正确率均为100%;相比于经典差值直方图平移方法,本算法的嵌入容量提升了43.7%;本算法在保证高容量的同时,PSNR达到42 dB以上,确保了嵌入图像失真较小。展开更多
文摘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.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61762054,U1736215,61772573 and 61563022in part by the National Science Foundation for Distinguished Young Scholars of Jiangxi Province under Grant 20171BCB23072Many thanks to the anonymous reviewers for their insightful comments and valuable suggestions,which helped a lot to improve the paper quality.
文摘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.
文摘为解决可逆信息隐藏(reversible data hiding,RDH)容量受限的问题,提出了一种基于相邻均值差的可逆信息隐藏(neighboring mean difference reversible data hiding,NMDRDH)算法。相邻均值差(neighboring mean diffe-rence,NMD):计算两个相邻数值的平均值与其中一个数值的差值。NMD将差值最小化,使数据更加集中。首先将图像进行分块,然后在分块上应用NMD生成差值直方图,最后通过平移差值直方图,利用峰值点来嵌入秘密信息。由于NMD使生成的差值直方图具有更多的峰值点,所以该方法可嵌入更多的秘密信息。实验结果表明,采用本算法,原始图像恢复率和秘密信息提取正确率均为100%;相比于经典差值直方图平移方法,本算法的嵌入容量提升了43.7%;本算法在保证高容量的同时,PSNR达到42 dB以上,确保了嵌入图像失真较小。
