基于机器学习的多用途医学图像水印。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rishi Sinhal, Irshad Ahmad Ansari
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引用次数: 1

摘要

由于目前数据量巨大,数字数据安全已成为一个迫切需要研究的领域。一些领域,如医疗成像和通过通信平台共享医疗数据,需要高度安全性,以防止伪造访问、操纵和其他处理操作。这一点至关重要,因为更改/操纵的数据可能会导致医学专家的错误判断,并对人类健康产生负面影响。该工作提供了一个使用深度神经网络的盲鲁棒医学图像水印框架,为医学图像提供了有效的安全解决方案。在水印过程中,通过采用LZW(Lampel-Ziv-Welch)压缩算法来保留原始图像的感兴趣区域(ROI)数据。随后,使用基于IWT(整数小波变换)的嵌入方法将鲁棒水印插入到原始图像中。接下来,针对ROI和RONI(非感兴趣区域)区域生成基于SHA-256算法的散列密钥。然后通过ROI恢复数据和散列密钥来准备脆弱水印。此外,利用基于LSB替换的插入机制将脆弱水印嵌入到鲁棒水印图像的RONI嵌入区域中。使用基于深度神经网络的框架来执行鲁棒水印提取,以获得计算时间较少的有效结果。仿真结果验证了该方案具有显著的不可见性、高效的鲁棒水印提取、正确的认证和完全可逆的ROI恢复特性。与现有方案的相对调查证实了拟议工作相对于现有工作的主导地位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning based multipurpose medical image watermarking.

Machine learning based multipurpose medical image watermarking.

Machine learning based multipurpose medical image watermarking.

Machine learning based multipurpose medical image watermarking.

Digital data security has become an exigent area of research due to a huge amount of data availability at present time. Some of the fields like medical imaging and medical data sharing over communication platforms require high security against counterfeit access, manipulation and other processing operations. It is essential because the changed/manipulated data may lead to erroneous judgment by medical experts and can negatively influence the human's heath. This work offers a blind and robust medical image watermarking framework using deep neural network to provide effective security solutions for medical images. During watermarking, the region of interest (ROI) data of the original image is preserved by employing the LZW (Lampel-Ziv-Welch) compression algorithm. Subsequently the robust watermark is inserted into the original image using IWT (integer wavelet transform) based embedding approach. Next, the SHA-256 algorithm-based hash keys are generated for ROI and RONI (region of non-interest) regions. The fragile watermark is then prepared by ROI recovery data and the hash keys. Further, the LSB replacement-based insertion mechanism is utilized to embed the fragile watermark into RONI embedding region of robust watermarked image. A deep neural network-based framework is used to perform robust watermark extraction for efficient results with less computational time. Simulation results verify that the scheme has significant imperceptibility, efficient robust watermark extraction, correct authentication and completely reversible nature for ROI recovery. The relative investigation with existing schemes confirms the dominance of the proposed work over already existing work.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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