基于重编码策略的隐写图像隐藏方法

Yuching Lu, G. Chakraborty, T. Lu
{"title":"基于重编码策略的隐写图像隐藏方法","authors":"Yuching Lu, G. Chakraborty, T. Lu","doi":"10.1109/ICAWST.2017.8256425","DOIUrl":null,"url":null,"abstract":"In normal Steganography, the main motivation is to maintain high quality of the stego image, so that one is not suspicious that there is a hidden image. At times, it is important that the hidden image quality needs to be maintained after recovery. How to achieve high quality of hidden image is the motivation of this work. To solve this problem, before the hiding procedure, pixels of the secret image are analyzed to generate the optimum code book. Most frequently occurring pixel value is encoded with the shortest code to minimize stego-image distortion. For evaluation, we not only tested the distortion of simple hidden images like a logo, but also high resolution images. Using the proposed method, PSNR value of more than 45.6 db for the stedo-image was achieved even for high resolution hidden image. Secret image PSNR after recovery was 50 db or more. We could conclude that the proposed method can yield good results regardless of the type of hidden image.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hidden content quality aware stego-image hiding method using re-encoding strategy\",\"authors\":\"Yuching Lu, G. Chakraborty, T. Lu\",\"doi\":\"10.1109/ICAWST.2017.8256425\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In normal Steganography, the main motivation is to maintain high quality of the stego image, so that one is not suspicious that there is a hidden image. At times, it is important that the hidden image quality needs to be maintained after recovery. How to achieve high quality of hidden image is the motivation of this work. To solve this problem, before the hiding procedure, pixels of the secret image are analyzed to generate the optimum code book. Most frequently occurring pixel value is encoded with the shortest code to minimize stego-image distortion. For evaluation, we not only tested the distortion of simple hidden images like a logo, but also high resolution images. Using the proposed method, PSNR value of more than 45.6 db for the stedo-image was achieved even for high resolution hidden image. Secret image PSNR after recovery was 50 db or more. We could conclude that the proposed method can yield good results regardless of the type of hidden image.\",\"PeriodicalId\":378618,\"journal\":{\"name\":\"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAWST.2017.8256425\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAWST.2017.8256425","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在正常的隐写术中,主要的动机是保持隐写图像的高质量,这样人们就不会怀疑有隐藏的图像。有时,需要在恢复后保持隐藏图像的质量是很重要的。如何实现高质量的隐藏图像是这项工作的动力。为了解决这一问题,在隐藏之前,对秘密图像的像素进行分析,生成最优的码本。使用最短的编码对最频繁出现的像素值进行编码,以减少隐写图像失真。为了评估,我们不仅测试了简单的隐藏图像(如徽标)的失真,还测试了高分辨率图像的失真。采用该方法,即使对于高分辨率的隐藏图像,也能获得超过45.6 db的PSNR值。恢复后的秘密图像PSNR在50 db以上。我们可以得出结论,无论隐藏图像的类型如何,所提出的方法都可以产生良好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Hidden content quality aware stego-image hiding method using re-encoding strategy
In normal Steganography, the main motivation is to maintain high quality of the stego image, so that one is not suspicious that there is a hidden image. At times, it is important that the hidden image quality needs to be maintained after recovery. How to achieve high quality of hidden image is the motivation of this work. To solve this problem, before the hiding procedure, pixels of the secret image are analyzed to generate the optimum code book. Most frequently occurring pixel value is encoded with the shortest code to minimize stego-image distortion. For evaluation, we not only tested the distortion of simple hidden images like a logo, but also high resolution images. Using the proposed method, PSNR value of more than 45.6 db for the stedo-image was achieved even for high resolution hidden image. Secret image PSNR after recovery was 50 db or more. We could conclude that the proposed method can yield good results regardless of the type of hidden image.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Deep convolutional neural network classifier for travel patterns using binary sensors Establishing the application of personal healthcare service system for cancer patients Disaster state information management gis system based on tiled diplay environment Keynote speech I: Big data, non-big data, and algorithms for recognizing the real world data Improving the performance of lossless reversible steganography via data sharing
×
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