Content-adaptive reliable robust lossless data embedding
An, Lingling3; Gao, Xinbo2,3; Yuan, Yuan1; Tao, Dacheng4; Deng, Cheng3; Ji, Feng3
刊名neurocomputing
2012-03-01
卷号79页码:1-11
关键词Histogram rotation Just noticeable distortion Robust lossless data embedding
ISSN号0925-2312
产权排序2
合作状况国际
中文摘要it is well known that robust lossless data embedding (rlde) methods can be used to protect copyright of digital images when the intactness of host images is highly demanded and the unintentional attacks may be encountered in data communication. however, the existing rlde methods cannot be applied satisfactorily to the practical scenarios due to different drawbacks, e.g., serious "salt-and-pepper" noise, low capacity and unreliable reversibility. in this paper, we propose an effective solution to rlde by improving the histogram rotation (hr)-based embedding model. the proposed method is a content-adaptive reliable rlde or car for short. it eliminates the "salt-and-pepper" noise in hr by the pixel adjustment mechanism. therefore, reliable regions for embedding can be well constructed. furthermore, we basically expect the watermark strengths to be adaptive to different image contents, and thus we have a chance to make an effective tradeoff between invisibility and robustness. the luminance masking together with the threshold strategy is duly adopted in the proposed rlde method, so the just noticeable distortion thresholds of different local regions can be well utilized to control the watermark strengths. experimental evidence on 300 test images including natural, medical and synthetic aperture radar (sar) images demonstrates the effectiveness of the proposed data embedding method.
英文摘要it is well known that robust lossless data embedding (rlde) methods can be used to protect copyright of digital images when the intactness of host images is highly demanded and the unintentional attacks may be encountered in data communication. however, the existing rlde methods cannot be applied satisfactorily to the practical scenarios due to different drawbacks, e.g., serious "salt-and-pepper" noise, low capacity and unreliable reversibility. in this paper, we propose an effective solution to rlde by improving the histogram rotation (hr)-based embedding model. the proposed method is a content-adaptive reliable rlde or car for short. it eliminates the "salt-and-pepper" noise in hr by the pixel adjustment mechanism. therefore, reliable regions for embedding can be well constructed. furthermore, we basically expect the watermark strengths to be adaptive to different image contents, and thus we have a chance to make an effective tradeoff between invisibility and robustness. the luminance masking together with the threshold strategy is duly adopted in the proposed rlde method, so the just noticeable distortion thresholds of different local regions can be well utilized to control the watermark strengths. experimental evidence on 300 test images including natural, medical and synthetic aperture radar (sar) images demonstrates the effectiveness of the proposed data embedding method. (c) 2011 elsevier b.v. all rights reserved.
学科主题computer science ; artificial intelligence
WOS标题词science & technology ; technology
类目[WOS]computer science, artificial intelligence
研究领域[WOS]computer science
关键词[WOS]statistical quantity histogram ; digital watermarking ; image watermarking
收录类别SCI ; EI
语种英语
WOS记录号WOS:000300138900001
公开日期2012-09-03
内容类型期刊论文
源URL[http://ir.opt.ac.cn/handle/181661/20246]  
专题西安光学精密机械研究所_光学影像学习与分析中心
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China
2.Xidian Univ, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China
3.Xidian Univ, Sch Elect Engn, VIPS Lab, Xian 710071, Peoples R China
4.Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia
推荐引用方式
GB/T 7714
An, Lingling,Gao, Xinbo,Yuan, Yuan,et al. Content-adaptive reliable robust lossless data embedding[J]. neurocomputing,2012,79:1-11.
APA An, Lingling,Gao, Xinbo,Yuan, Yuan,Tao, Dacheng,Deng, Cheng,&Ji, Feng.(2012).Content-adaptive reliable robust lossless data embedding.neurocomputing,79,1-11.
MLA An, Lingling,et al."Content-adaptive reliable robust lossless data embedding".neurocomputing 79(2012):1-11.
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