基于加速kaze离散余弦变换的医学图像鲁棒水印算法

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
IET Biometrics Pub Date : 2022-10-12 DOI:10.1049/bme2.12102
Dekai Li, Yen-wei Chen, Jingbing Li, Lei Cao, Uzair Aslam Bhatti, Pengju Zhang
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引用次数: 3

摘要

随着互联网技术领域的不断进步和发展,医学图像处理领域也随之发展起来。特别是数字水印技术在医学图像处理领域发挥着至关重要的作用,极大地提高了医学图像信息的安全性。针对医学图像水印算法对几何攻击鲁棒性差导致医学图像信息安全性低的问题,提出了一种基于加速kaze离散余弦变换(AKAZE-DCT)的医学图像水印算法。首先利用AKAZE-DCT算法提取医学图像的特征向量,然后结合感知哈希技术得到医学图像的特征序列;然后对水印图像进行逻辑混沌错位加密,得到加密后的水印图像,保证了水印信息的安全性;最后,采用零水印技术对水印进行嵌入和提取。实验结果表明,该算法在常规攻击和几何攻击下均能有效提取水印,具有较好的鲁棒性和不可见性,与其他算法相比,在医学领域具有一定的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Robust watermarking algorithm for medical images based on accelerated-KAZE discrete cosine transform

Robust watermarking algorithm for medical images based on accelerated-KAZE discrete cosine transform

With the continuous progress and development in the field of Internet technology, the area of medical image processing has also developed along with it. Specially, digital watermarking technology plays an essential role in the field of medical image processing and greatly improves the security of medical image information. A medical image watermarking algorithm based on an accelerated-KAZE discrete cosine transform (AKAZE-DCT) is proposed to address the poor robustness of medical image watermarking algorithms to geometric attacks, which leads to low security of the information contained in medical images. First, the AKAZE-DCT algorithm is used to extract the feature vector of the medical image and then combined with the perceptual hashing technique to obtain the feature sequence of the medical image; then, the watermarking image is encrypted with logistic chaos dislocation to get the encrypted watermarking image, which ensures the security of the watermarking information; finally, the watermarking is embedded and extracted with the zero-watermarking technique. The experimental results show that the algorithm can effectively extract the watermark under conventional and geometric attacks, reflecting better robustness and invisibility, and has certain practicality in the medical field compared with other algorithms.

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来源期刊
IET Biometrics
IET Biometrics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
0.00%
发文量
46
审稿时长
33 weeks
期刊介绍: The field of biometric recognition - automated recognition of individuals based on their behavioural and biological characteristics - has now reached a level of maturity where viable practical applications are both possible and increasingly available. The biometrics field is characterised especially by its interdisciplinarity since, while focused primarily around a strong technological base, effective system design and implementation often requires a broad range of skills encompassing, for example, human factors, data security and database technologies, psychological and physiological awareness, and so on. Also, the technology focus itself embraces diversity, since the engineering of effective biometric systems requires integration of image analysis, pattern recognition, sensor technology, database engineering, security design and many other strands of understanding. The scope of the journal is intentionally relatively wide. While focusing on core technological issues, it is recognised that these may be inherently diverse and in many cases may cross traditional disciplinary boundaries. The scope of the journal will therefore include any topics where it can be shown that a paper can increase our understanding of biometric systems, signal future developments and applications for biometrics, or promote greater practical uptake for relevant technologies: Development and enhancement of individual biometric modalities including the established and traditional modalities (e.g. face, fingerprint, iris, signature and handwriting recognition) and also newer or emerging modalities (gait, ear-shape, neurological patterns, etc.) Multibiometrics, theoretical and practical issues, implementation of practical systems, multiclassifier and multimodal approaches Soft biometrics and information fusion for identification, verification and trait prediction Human factors and the human-computer interface issues for biometric systems, exception handling strategies Template construction and template management, ageing factors and their impact on biometric systems Usability and user-oriented design, psychological and physiological principles and system integration Sensors and sensor technologies for biometric processing Database technologies to support biometric systems Implementation of biometric systems, security engineering implications, smartcard and associated technologies in implementation, implementation platforms, system design and performance evaluation Trust and privacy issues, security of biometric systems and supporting technological solutions, biometric template protection Biometric cryptosystems, security and biometrics-linked encryption Links with forensic processing and cross-disciplinary commonalities Core underpinning technologies (e.g. image analysis, pattern recognition, computer vision, signal processing, etc.), where the specific relevance to biometric processing can be demonstrated Applications and application-led considerations Position papers on technology or on the industrial context of biometric system development Adoption and promotion of standards in biometrics, improving technology acceptance, deployment and interoperability, avoiding cross-cultural and cross-sector restrictions Relevant ethical and social issues
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