用于加密图像中可逆数据隐藏的选择性 bin 模型

IF 3.7 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Analysis and Applications Pub Date : 2024-02-28 DOI:10.1007/s10044-024-01220-z
Ruchi Agarwal, Sara Ahmed, Manoj Kumar
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引用次数: 0

摘要

随着技术的飞速发展,在互联网上安全传输数据的问题也变得越来越重要。在数字媒体中,将数据封装在图像中是传递机密信息最常用的方法之一。本文提出了一种基于选择性 bin 模型的新型加密图像可逆数据隐藏方案。该方案的重点是提高嵌入能力,同时借助加密和拟议的数据隐藏过程确保图像的安全性。为了进行数据嵌入,采用了无损压缩技术,并将图像分为三个分区。然后,为这些分区分配标记位,以区分可嵌入和不可嵌入区域。由于采用了选择性分仓方法,所提出的方法对于平滑图像和复杂图像都能显示出令人满意的嵌入率。此外,该方法还具有可分性,即数据提取和图像复原可以独立进行。此外,与其他方法相比,实验结果证明了该策略的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Selective bin model for reversible data hiding in encrypted images

In tandem with the fast-growing technology, the issue of secure data transmission over the Internet has achieved increasing importance. In digital media, enclosing data in images is one of the most common methods for communicating confidential information. A novel reversible data hiding in the encrypted images scheme based on selective bin models is proposed in this paper. The scheme focuses on enhancing the embedding capacity while ensuring the security of images with the help of encryption and the proposed data hiding process. For data embedding, lossless compression is utilized and the image is classified into three bins. Then, marker bits are assigned to these bins for distinguishing between embeddable and non-embeddable regions. The proposed method shows a satisfactory embedding rate for smooth images as well as complex ones due to its selective bin approach. Also, the method is separable in nature, i.e., data extraction and image recovery can be performed independently. Furthermore, the experimental results demonstrate the strategy’s effectiveness when compared with others.

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来源期刊
Pattern Analysis and Applications
Pattern Analysis and Applications 工程技术-计算机:人工智能
CiteScore
7.40
自引率
2.60%
发文量
76
审稿时长
13.5 months
期刊介绍: The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.
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