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A novel robust zero-watermarking algorithm for medical images

数字水印 水印 计算机科学 加密 稳健性(进化) 人工智能 特征(语言学) 计算机视觉 模式识别(心理学) 图像(数学) 奇异值分解 特征提取 算法 认证(法律)
作者
Kun Hu,Xiaochao Wang,Jianping Hu,Hongfei Wang,Hong Qin
出处
期刊:The Visual Computer [Springer Science+Business Media]
卷期号:37 (9-11): 2841-2853 被引量:1
标识
DOI:10.1007/s00371-021-02168-5
摘要

A novel robust zero-watermarking algorithm for medical images is presented in this paper. The multi-scale decomposition of bi-dimensional empirical mode decomposition (BEMD) has exhibited many attractive properties that enable the proposed algorithm to robustly detect the tampering regions and protect the copyright of medical images simultaneously. Given a medical image, we first decompose a medical image adaptively into a finite number of intrinsic mode functions (IMFs) and a residue, by taking a full advantage of BEMD. The first IMF starts with the finest scale retaining fragile information and is best suitable for tampering detection, while the residue includes robust information at the coarser scale and is applied to the protection of intellectual property rights of medical images. Next, the feature matrices are extracted from the first IMF and the residue via singular value decomposition, which achieves robust performance subject to most attacks. For a given watermark image, it is encrypted by Arnold transform to enhance the security of the watermark. Then, the feature images are constructed by performing the exclusive-or operation between the encrypted watermark image and the extracted feature matrices. Finally, the feature images are securely stored in the copyright authentication database to be further used for copyright authentication and tampering detection. A large number of experimental results and comparisons with existing watermarking algorithms confirm that the newly proposed watermarking algorithm not only has strong ability on tampering detection, but also has better performance in combating various attacks, including cropping, Gaussian noise, median filtering, image enhancement attacks, etc. The newly developed algorithm also shows great promise in processing natural images.
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