Independent Embedding Domain Based Two-Stage Robust Reversible Watermarking

稳健性(进化) 嵌入 数字水印 计算机科学 水印 领域(数学分析) 图像(数学) 算法 人工智能 数学 生物化学 基因 数学分析 化学
作者
Xiang Wang,Xiaolong Li,Qingqi Pei
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology [Institute of Electrical and Electronics Engineers]
卷期号:30 (8): 2406-2417 被引量:57
标识
DOI:10.1109/tcsvt.2019.2915116
摘要

Robustness is the most important factor that limits the practical application of reversible watermarking. To deal with this issue, several robust reversible watermarking (RRW) techniques have been proposed. Among them, the two-stage RRW framework proposed by Coltuc et al. is a promising one. In the first state of this framework, a robust watermark is embedded into the cover image to provide robustness, and then in the second stage, the information enabling revert the robust embedding is reversibly embedded into the already marked image to guarantee the reversibility. However, because of using the same area for these two embedding stages, the robustness in the first stage is seriously weakened by the reversible embedding. As a result, this elegant method is not effective as expected. Based on this consideration, this paper proposes an independent embedding domain (ED)-based two-stage RRW. The cover image is first transformed into two independent EDs, and then the robust and reversible watermarks are embedded into each domain separately. The carrier derived from the first embedding stage that carrying the robust watermark will not change after the reversible embedding, and thus, the robustness of the first stage is well preserved. By the proposed method, the embedding performance of the original two-stage RRW is significantly enhanced. Moreover, the proposed method is experimentally verified better than some other state-of-the-art RRW methods.
最长约 10秒,即可获得该文献文件

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
TanFT发布了新的文献求助10
刚刚
青鸟飞鱼完成签到,获得积分10
刚刚
吴吴发布了新的文献求助10
1秒前
ShengjuChen完成签到 ,获得积分10
1秒前
1秒前
CipherSage应助标致小伙采纳,获得10
1秒前
科研通AI5应助深爱不疑采纳,获得10
1秒前
艺术家脾气完成签到,获得积分10
2秒前
3秒前
unicornmed发布了新的文献求助10
3秒前
可爱的函函应助茶艺如何采纳,获得10
4秒前
江知之完成签到 ,获得积分0
4秒前
4秒前
6秒前
刻苦问柳发布了新的文献求助10
6秒前
酷酷平卉完成签到 ,获得积分10
6秒前
星辰大海应助吴吴采纳,获得30
6秒前
JTB发布了新的文献求助10
6秒前
BILNQPL发布了新的文献求助10
6秒前
兮遥遥完成签到 ,获得积分10
7秒前
7秒前
7秒前
丰知然应助轩辕德地采纳,获得10
8秒前
9秒前
吨吨喝水关注了科研通微信公众号
9秒前
酷波er应助某只橘猫君采纳,获得10
9秒前
9秒前
stt发布了新的文献求助10
9秒前
9秒前
Ling完成签到,获得积分10
9秒前
TanFT完成签到,获得积分10
10秒前
笙歌自若发布了新的文献求助10
10秒前
10秒前
CipherSage应助积极的凌波采纳,获得10
11秒前
11秒前
烟花应助欣慰硬币采纳,获得10
11秒前
老大爷滴滴完成签到,获得积分10
11秒前
11秒前
11秒前
SciGPT应助LEMON采纳,获得10
12秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527723
求助须知:如何正确求助?哪些是违规求助? 3107826
关于积分的说明 9286663
捐赠科研通 2805577
什么是DOI,文献DOI怎么找? 1539998
邀请新用户注册赠送积分活动 716878
科研通“疑难数据库(出版商)”最低求助积分说明 709762