Region-based colour image spatial fusion steganographic system

P. Viswanathan, P. Krishna
{"title":"Region-based colour image spatial fusion steganographic system","authors":"P. Viswanathan, P. Krishna","doi":"10.1504/IJMIS.2018.10017640","DOIUrl":null,"url":null,"abstract":"The steganographic schemes embed secret data in the location of cover image generated by pseudo random number generator. These kinds of algorithms do not evaluate the content and size of the secret message during embedding. It creates distortion in smooth region of cover image and results in secret data loss. In order to solve the above issues, high-capacity novel region-based spatial fusion steganographic scheme is proposed in this paper. This spatial fusion decomposes the image into various regions based on the level of intensity. It is extensively used to evaluate the capacity of region for high payload. Size and type of secret data is initialised as a key for the extraction to improve the confidentiality and privacy. Then the secret message with key is embedded by differentiating the pixel by 1 with constraints not equal to 0 and even or odd option which leads to less rate of modification. The proposed scheme is applied in the region of blue channel which is less sensitive to human visual system gives high imperceptibility to secret data. It is experimented with standard IST natural images by steganalysis algorithms results in better enhancement of security and quality in terms of imperceptibility and embedding capacity when compare to other LSB approaches.","PeriodicalId":312177,"journal":{"name":"Int. J. Multim. Intell. Secur.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Multim. Intell. Secur.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJMIS.2018.10017640","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The steganographic schemes embed secret data in the location of cover image generated by pseudo random number generator. These kinds of algorithms do not evaluate the content and size of the secret message during embedding. It creates distortion in smooth region of cover image and results in secret data loss. In order to solve the above issues, high-capacity novel region-based spatial fusion steganographic scheme is proposed in this paper. This spatial fusion decomposes the image into various regions based on the level of intensity. It is extensively used to evaluate the capacity of region for high payload. Size and type of secret data is initialised as a key for the extraction to improve the confidentiality and privacy. Then the secret message with key is embedded by differentiating the pixel by 1 with constraints not equal to 0 and even or odd option which leads to less rate of modification. The proposed scheme is applied in the region of blue channel which is less sensitive to human visual system gives high imperceptibility to secret data. It is experimented with standard IST natural images by steganalysis algorithms results in better enhancement of security and quality in terms of imperceptibility and embedding capacity when compare to other LSB approaches.
基于区域的彩色图像空间融合隐写系统
隐写方案将秘密数据嵌入到伪随机数生成器生成的封面图像的位置。这些算法在嵌入过程中不评估秘密消息的内容和大小。它会造成封面图像平滑区域的失真,导致机密数据丢失。为了解决上述问题,本文提出了一种基于区域的高容量空间融合隐写方案。这种空间融合基于强度水平将图像分解为不同的区域。它被广泛地用于评估高载荷区域的能力。初始化秘密数据的大小和类型作为提取的密钥,以提高机密性和隐私性。然后,通过约束不等于0和偶数或奇数选项将像素微分1来嵌入带密钥的秘密消息,从而降低修改率。该方案应用于人眼视觉敏感度较低的蓝色通道区域,对秘密数据具有较高的隐蔽性。用隐写分析算法对标准IST自然图像进行了实验,结果表明,与其他LSB方法相比,隐写分析在隐秘性和嵌入容量方面提高了安全性和质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信