Image Steganography System based on Hybrid Edge Detector

Habiba Sultana, A. Kamal
{"title":"Image Steganography System based on Hybrid Edge Detector","authors":"Habiba Sultana, A. Kamal","doi":"10.1109/ICCIT54785.2021.9689777","DOIUrl":null,"url":null,"abstract":"In the field of image steganography, edge detection based implantation methods play vital rules in providing stronger security of hided data. In this arena, researcher applies a suitable edge detection method to detect edge pixels in an image. Those detected pixels then conceive secret message bits. A very recent trend is to employ multiple edge detection methods to increase edge pixels in an image and thus to enhance the embedding capacity. The uses of multiple edge detectors additionally boost up the data security. Like as the demand for embedding capacity, many applications need to have the modified image, i.e., stego image, with good quality. Indeed, when the message payload is low, it will not be a better idea to finds more local pixels for embedding that small payload. Rather, the image quality will look better, visually and statistically, if we could choose a part but sufficient pixels to implant bits. In this article, we propose an algorithm that uses multiple edge detection algorithms to find edge pixels separately and then selects pixels which are common to all edges. This way, the proposed method decreases the number of embeddable pixels and thus, increases the image quality. The experimental results provide promising output.","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 24th International Conference on Computer and Information Technology (ICCIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIT54785.2021.9689777","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In the field of image steganography, edge detection based implantation methods play vital rules in providing stronger security of hided data. In this arena, researcher applies a suitable edge detection method to detect edge pixels in an image. Those detected pixels then conceive secret message bits. A very recent trend is to employ multiple edge detection methods to increase edge pixels in an image and thus to enhance the embedding capacity. The uses of multiple edge detectors additionally boost up the data security. Like as the demand for embedding capacity, many applications need to have the modified image, i.e., stego image, with good quality. Indeed, when the message payload is low, it will not be a better idea to finds more local pixels for embedding that small payload. Rather, the image quality will look better, visually and statistically, if we could choose a part but sufficient pixels to implant bits. In this article, we propose an algorithm that uses multiple edge detection algorithms to find edge pixels separately and then selects pixels which are common to all edges. This way, the proposed method decreases the number of embeddable pixels and thus, increases the image quality. The experimental results provide promising output.
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
基于混合边缘检测器的图像隐写系统
在图像隐写领域,基于边缘检测的植入方法对于增强隐藏数据的安全性起着至关重要的作用。在这一领域,研究者采用一种合适的边缘检测方法来检测图像中的边缘像素。这些被检测到的像素然后孕育出秘密信息位。最近的一个趋势是采用多种边缘检测方法来增加图像中的边缘像素,从而提高嵌入能力。多个边缘检测器的使用也提高了数据的安全性。就像对嵌入容量的需求一样,许多应用都需要得到质量好的修改后的图像,即隐写图像。实际上,当消息有效负载较低时,寻找更多的本地像素来嵌入小的有效负载并不是一个更好的主意。相反,如果我们可以选择一个部分,但有足够的像素来植入比特,那么图像质量在视觉上和统计上都会看起来更好。在本文中,我们提出了一种算法,该算法使用多个边缘检测算法分别找到边缘像素,然后选择所有边缘共有的像素。这样,该方法减少了可嵌入像素的数量,从而提高了图像质量。实验结果提供了令人满意的输出。
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
自引率
0.00%
发文量
0
期刊最新文献
The Eigenvalue Distribution of Hankel Matrix: A Tool for Spectral Estimation From Noisy Data Demystify the Black-box of Deep Learning Models for COVID-19 Detection from Chest CT Radiographs Machine Learning Techniques to Precaution of Emerging Disease in the Poultry Industry A Framework for Multi-party Skyline Query Maintaining Privacy and Data Integrity Application of Feature based Face Detection in Adaptive Skin Pixel Identification Using Signal Processing Techniques
×
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