期刊文献+

基于支持向量机与结构相似度的图像数字水印算法

An Image Watermark Algorithm Based on SVM and Structural Similarity
下载PDF
导出
摘要 针对空域数字水印算法鲁棒性差、难以抵抗较强的攻击的问题,本文提出一种基于支持向量机SVM与结构相似度SSIM的自适应图像数字水印算法。本文利用结构相似度算法SSIM计算不同图像子块的最大水印嵌入强度,通过回归性支持向量机建立不同图像子块与最大水印嵌入强度的相关性模型,实现了根据不同图像子块预测水印嵌入强度。本文在现有基于图像邻域像素之间相关性的时空域数字水印算法的基础上,选取图像中的图像子块进行水印嵌入,通过修改子块中心位置像素值,进行水印嵌入与提取。本文提出的算法在确保水印算法具有较好的透明性的基础上,提高了水印算法的鲁棒性。实验结果表明,该算法在保持较好透明性的基础上,对于JPEG压缩、噪声、中值滤波等攻击具有较强的抵抗能力。 A watermark algorithm based on SVM and structural similarity is proposed in this paper to improve robustness of spatial domain watermark technology. Structural similarity is used to calculate the largest embedding strength for different image blocks and SVM is used to build model which was able to predict the relationship between image block and embedding strength. The proposed algorithm embeds and extracts watermark by modifying central pixel of blocks selected from image. The experimental results show that the proposed algorithm is robust enough to resist multiple watermark attack including JPEG compress, noise add and median iflter.
出处 《软件》 2013年第8期112-115,119,共5页 Software
基金 中央高校基本科研业务专项资金资助(BUPT2012RC0217)
关键词 计算机应用技术 自适应水印算法 支持向量机 结构相似度 鲁棒性 Computer application technology Self-adaptive watermark SVM Structural similarity Robustness
  • 相关文献

参考文献12

  • 1杨义先;钮心忻.数字水印理论与技术[M]{H}北京:高等教育出版社,2006.
  • 2Yu P T,Tsai H H,Lin J S. Digital watermarking based on neural networks for color images[J].{H}SIGNAL PROCESSING,2001,(03):663-671.
  • 3Fu Y,Shen R,Lu H. Optimal watermark detection based on support vector machines[A].Springer Berlin Heidelberg,2004.552-557.
  • 4Wang H,Mao L,Xiu K. New audio embedding technique based on neural network[A].{H}IEEE,2006.459-462.
  • 5Shen R,Fu Y,Lu H. A novel image watermarking scheme based on support vector regression[J].{H}JOURNAL OF SYSTEMS AND SOFTWARE,2005,(01):1-8.
  • 6李春花,卢正鼎.一种基于支持向量机的图像数字水印算法[J].中国图象图形学报,2006,11(9):1322-1326. 被引量:23
  • 7Vapnik V N. The Nature of Statistical Learning Theory[M].{H}New York:Springer-verlag,1995.
  • 8Watson A B. Digital images and human vision[M].MIT press,1993.
  • 9Citti,Giovanna,Alessandro Sarti. A cortical based model of perceptual completion in the roto-translation space[J].{H}JOURNAL OF MATHEMATICAL IMAGING AND VISION,2006,(03):307-326.
  • 10Wang,Zhou,Eero P.Simoncelli,Alan C.Bovik. Multiscale structural similarity for image quality assessment[A].{H}IEEE,2003.

二级参考文献11

  • 1van Schyndel R G, Tirkel A Z, Osborne C F. A digital watermark[A]. In: Proceedings of the IEEE International Conference on Image Processing[C], Austin, Texas, USA, 1994, 2: 86-90.
  • 2Nikolaidis N, Pitas I. Copyright protection of images using robust digital signatures [A]. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing [C],Atlanta, Georgia, USA, 1996, 4:2168-2171.
  • 3Cox I J, Kilian J, Leighton F T, et al. Secure spread spectrum watermarking for multimedia [J]. IEEE Transactions on Image Processing. 1997, 6(12): 1673-1687.
  • 4Hsu Chion-ting, Wu Ja-ling. Multiresolution watermarking for digital image[J]. IEEE Transactions on Circuits and Systems Ⅱ: Analog and Digital Signal Processing, 1998, 45 (8): 1097-1101.
  • 5Lou Der-chyuan, Liu Jiang-lung, Hu Ming-chiang. Adaptive digital watermarking using neural network technique[A]. In: Proceedings of the IEEE International Carnahan Conference on Security Technology[C], Taipei, Taiwan, 2003: 325-332.
  • 6Davis K J, Najarian K. Maximizing strength of digital watermarks using neural networks[A]. In: Proceedings of the International Joint Conference on Neural Networks [C], Washington DC, USA, 2001,4:2893-2898.
  • 7Shieh C S, Huang H C, Wang F H, et al. Genetic watermarking based on transform-domain techniques [J]. Pattern Recognition,2004, 37(3): 555-565.
  • 8Pereira S, Voloshynoskiy S, Pun T. Optimal transform domains watermark embedding via linear programming[J]. Signal Processing,2001, 81(6):1251-1260.
  • 9[美]Vapnik V N著,张学工译.统计学习理论的本质[M].北京:清华大学出版社,2000.
  • 10Li Chun-hua, Lu Zheng-ding, Zhou Ke. SVR-Parameters Selection for Image watermarking [A]. In: Proceedings of 17th IEEE International Conference on Tools with Artificial Intelligence [C],Hongkong, China, 2005: 466-470.

共引文献25

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部