Interpolation Based Reversible Data Hiding using Pixel Intensity Classes

Abhinandan Tripathi, Jay Prakash
{"title":"Interpolation Based Reversible Data Hiding using Pixel Intensity Classes","authors":"Abhinandan Tripathi, Jay Prakash","doi":"10.47164/ijngc.v14i4.1170","DOIUrl":null,"url":null,"abstract":"In this article, we suggest a new interpolation technique as well as a novel Reversible Data Hiding (RDH) approach for up scaling the actual image and concealing sensitive information within the up scaled/interpolated image. This data hiding strategy takes into account the features of the Human Visual System (HVS) when concealing the secret data in order to prevent detection of the private data even after extensive embedding. The private data bits are adaptively embedded into the picture cell based on its values in the suggested hiding strategy after grouping different pixel intensity ranges. As a result, the proposed approach can preserve the stego-visual image’s quality. According to experimental findings, the proposed interpolation approach achieves PSNRs of over 40 dB for all experimental images. The outcomes further demonstrate that the suggested data concealing strategy outperforms every other interpolation-based data hiding scheme existing in use.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"21 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Next-Generation Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47164/ijngc.v14i4.1170","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

In this article, we suggest a new interpolation technique as well as a novel Reversible Data Hiding (RDH) approach for up scaling the actual image and concealing sensitive information within the up scaled/interpolated image. This data hiding strategy takes into account the features of the Human Visual System (HVS) when concealing the secret data in order to prevent detection of the private data even after extensive embedding. The private data bits are adaptively embedded into the picture cell based on its values in the suggested hiding strategy after grouping different pixel intensity ranges. As a result, the proposed approach can preserve the stego-visual image’s quality. According to experimental findings, the proposed interpolation approach achieves PSNRs of over 40 dB for all experimental images. The outcomes further demonstrate that the suggested data concealing strategy outperforms every other interpolation-based data hiding scheme existing in use.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用像素强度等级进行基于插值的可逆数据隐藏
在本文中,我们提出了一种新的插值技术和新颖的可逆数据隐藏(RDH)方法,用于放大实际图像并在放大/插值图像中隐藏敏感信息。这种数据隐藏策略在隐藏秘密数据时考虑了人类视觉系统(HVS)的特征,以防止在大量嵌入后仍能检测到私人数据。在建议的隐藏策略中,私人数据位是根据不同像素强度范围分组后的值自适应嵌入图片单元的。因此,建议的方法可以保持偷窃视觉图像的质量。实验结果表明,建议的插值方法在所有实验图像中的 PSNR 都超过了 40 dB。实验结果进一步证明,建议的数据隐藏策略优于现有的其他基于插值的数据隐藏方案。
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 去求助
来源期刊
International Journal of Next-Generation Computing
International Journal of Next-Generation Computing COMPUTER SCIENCE, THEORY & METHODS-
自引率
66.70%
发文量
60
期刊最新文献
Integrating Smartphone Sensor Technology to Enhance Fine Motor and Working Memory Skills in Pediatric Obesity: A Gamified Approach Deep Learning based Semantic Segmentation for Buildings Detection from Remote Sensing Images Machine Learning-assisted Distance Based Residual Energy Aware Clustering Algorithm for Energy Efficient Information Dissemination in Urban VANETs High Utility Itemset Extraction using PSO with Online Control Parameter Calibration Alzheimer’s Disease Classification using Feature Enhanced Deep Convolutional Neural Networks
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1