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 Nature]
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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
SOL完成签到,获得积分10
1秒前
1秒前
酷酷的城完成签到,获得积分10
2秒前
向钱看完成签到 ,获得积分10
2秒前
HAHAHA完成签到,获得积分10
2秒前
sober完成签到,获得积分10
3秒前
character577完成签到,获得积分10
3秒前
脑洞疼应助Nimnse采纳,获得30
4秒前
CHENYINGYING发布了新的文献求助10
4秒前
丘比特应助wangli采纳,获得10
5秒前
精明芷巧完成签到 ,获得积分10
5秒前
didi完成签到,获得积分10
5秒前
短腿小柯基完成签到 ,获得积分10
6秒前
erjfuhe发布了新的文献求助10
6秒前
量子星尘发布了新的文献求助10
6秒前
草原狼完成签到,获得积分10
7秒前
zhang005on完成签到,获得积分10
7秒前
lixia完成签到 ,获得积分10
7秒前
小牧鱼完成签到,获得积分10
10秒前
11秒前
勤劳的斑马完成签到,获得积分10
11秒前
11秒前
kqd完成签到,获得积分10
12秒前
13秒前
Nimnse完成签到,获得积分10
14秒前
14秒前
15秒前
15秒前
junyang完成签到,获得积分10
15秒前
肖旻发布了新的文献求助10
15秒前
风雨季夏完成签到 ,获得积分10
15秒前
dyp完成签到,获得积分10
15秒前
开心的谷兰完成签到,获得积分10
16秒前
所所应助Sammy采纳,获得10
16秒前
落雪慕卿颜完成签到,获得积分10
16秒前
Nimnse发布了新的文献求助30
17秒前
12333发布了新的文献求助10
17秒前
量子星尘发布了新的文献求助10
17秒前
李爱国应助恃6采纳,获得10
18秒前
18秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Encyclopedia of Forensic and Legal Medicine Third Edition 5000
Introduction to strong mixing conditions volume 1-3 5000
Agyptische Geschichte der 21.30. Dynastie 3000
Aerospace Engineering Education During the First Century of Flight 2000
从k到英国情人 1700
„Semitische Wissenschaften“? 1510
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5773617
求助须知:如何正确求助?哪些是违规求助? 5612760
关于积分的说明 15431930
捐赠科研通 4906024
什么是DOI,文献DOI怎么找? 2640036
邀请新用户注册赠送积分活动 1587869
关于科研通互助平台的介绍 1542957