Image Hiding Based on Compressive Autoencoders and Normalizing Flow

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-09-19 DOI:10.1109/LSP.2024.3465350
Liang Chen;Xianquan Zhang;Chunqiang Yu;Zhenjun Tang
{"title":"Image Hiding Based on Compressive Autoencoders and Normalizing Flow","authors":"Liang Chen;Xianquan Zhang;Chunqiang Yu;Zhenjun Tang","doi":"10.1109/LSP.2024.3465350","DOIUrl":null,"url":null,"abstract":"Image hiding aims to hide the secret data in the cover image for secure transmission. Recently, with the development of deep learning, some deep learning-based image hiding methods were proposed. However, most of them do not achieve outstanding hiding performance yet. To address this issue, we propose a new image hiding framework called CAE-NF, which consists of compressive autoencoders (CAE) and normalizing flow (NF). Specifically, CAE's encoder respectively maps the secret image and cover image into the corresponding feature vectors. Image hiding and recovery can be modelled as the forward and backward processes of NF since NF is an invertible neural network. NF maps two feature vectors to a stego-image by its forward process. On the recovery side, the stego-images are mapped to two feature vectors by NF's backward process. Finally, the secret image is recovered by CAE's decoder. The proposed framework can achieve a good trade-off between the stego-image quality and recovered secret image quality, and meanwhile, improve the hiding and recovery performances. The experimental results demonstrate that the proposed framework significantly outperforms some state-of-the-art methods in terms of invisibility, security, and recovery accuracy on various datasets.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10684564/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Image hiding aims to hide the secret data in the cover image for secure transmission. Recently, with the development of deep learning, some deep learning-based image hiding methods were proposed. However, most of them do not achieve outstanding hiding performance yet. To address this issue, we propose a new image hiding framework called CAE-NF, which consists of compressive autoencoders (CAE) and normalizing flow (NF). Specifically, CAE's encoder respectively maps the secret image and cover image into the corresponding feature vectors. Image hiding and recovery can be modelled as the forward and backward processes of NF since NF is an invertible neural network. NF maps two feature vectors to a stego-image by its forward process. On the recovery side, the stego-images are mapped to two feature vectors by NF's backward process. Finally, the secret image is recovered by CAE's decoder. The proposed framework can achieve a good trade-off between the stego-image quality and recovered secret image quality, and meanwhile, improve the hiding and recovery performances. The experimental results demonstrate that the proposed framework significantly outperforms some state-of-the-art methods in terms of invisibility, security, and recovery accuracy on various datasets.
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
基于压缩自动编码器和归一化流的图像隐藏技术
图像隐藏的目的是将秘密数据隐藏在覆盖图像中,以实现安全传输。最近,随着深度学习的发展,人们提出了一些基于深度学习的图像隐藏方法。然而,大多数方法的隐藏性能并不突出。为了解决这个问题,我们提出了一种名为 CAE-NF 的新型图像隐藏框架,它由压缩自动编码器(CAE)和归一化流(NF)组成。具体来说,CAE 编码器分别将秘密图像和封面图像映射到相应的特征向量中。由于 NF 是一种可逆神经网络,因此图像隐藏和恢复可模拟为 NF 的前向和后向过程。NF 通过前向过程将两个特征向量映射为一个偷窃图像。在恢复方面,NF 的后向过程将偷窃图像映射为两个特征向量。最后,通过 CAE 解码器恢复秘密图像。所提出的框架可以很好地权衡隐去图像质量和恢复的秘密图像质量,同时提高隐藏和恢复性能。实验结果表明,在各种数据集上,所提出的框架在隐蔽性、安全性和恢复精度方面都明显优于一些最先进的方法。
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
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
期刊最新文献
KFA: Keyword Feature Augmentation for Open Set Keyword Spotting RFI-Aware and Low-Cost Maximum Likelihood Imaging for High-Sensitivity Radio Telescopes Audio Mamba: Bidirectional State Space Model for Audio Representation Learning System-Informed Neural Network for Frequency Detection Order Estimation of Linear-Phase FIR Filters for DAC Equalization in Multiple Nyquist Bands
×
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