基于改进视觉模型的自适应图像水印算法
Adaptive Image Watermarking Algorithm Based on Improved Perceptual Models

作者: 张毅锋 * , 蒋燕玲 , 裴文江 , 王 开 :东南大学信息科学与工程学院,南京;

关键词: 量化水印视觉模型扩展量化索引调制自适应量化Quantization Watermarking Perceptual Model F Spread Transform Quantization Index Modulation Adaptive Quantization

摘要: 基于量化的水印嵌入算法可以实现盲检测,QIM(Quantized Index Modulation)是最常见的量化嵌入方法。量化步长是影响量化水印算法性能的最重要因素之一。本论文基于视觉模型的特点,针对多种具体的攻击,提出了对视觉模型进一步改进以及改进视觉模型下的四种不同水印嵌入算法,并将其与QIM相结合。实验结果表明本论文提出的算法对噪声干扰和常见的图像处理均具有较好的鲁棒性。论文最后给出总结和展望。

Abstract: A blind detection can be achieved based on the quantization of the watermark embedding algorithm. QIM (Quantized Index Modulation) is one of the most common quantization embedding methods. The quantization step is one of the most important factors which affect the performance of quantization watermarkings. In this paper, according to the characteristic of perceptual model and a variety of attacks, further modified perceptual model and different imple- mentations of perceptual model are proposed. They are incorporated with the spread transform quantization index modulation (ST-QIM) framework. The experimental results show that the four algorithms we proposed in this paper are robust to noise attacks and common digital image processing operations. Finally, in conclusion section, summary and outlook are given.

文章引用: 张毅锋 , 蒋燕玲 , 裴文江 , 王 开 (2013) 基于改进视觉模型的自适应图像水印算法。 图像与信号处理, 2, 24-31. doi: 10.12677/jisp.2013.22004

参考文献

[1] X. B. Gao, C. Deng, X. L. Li and D. C. Tao. Geometric distor- tion insensitive image watermarking in affine covariant regions. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Ap-plications and Reviews, 2010, 40(3): 278-286.

[2] H.-C. Huang, W.-C. Fang. Metadata-based image watermarking for copyright protection. Simulation Modelling Practice and Theory, 2010, 18: 436-445.

[3] M. K. Khan, L. Xie and J. S. Zhang. Chaos and NDFT-based spread spectrum concealing of fingerprint-biometric data into audio signals. Digital Signal Processing, 2010, 20: 179-190.

[4] F. M. Bui, K. Martin, H. P. Lu, K. N. Plataniotis and D. Hatzi- nakos. Fuzzy key binding strategies based on quantization index modulation (QIM) for biometric encryption (BE) applications. IEEE Transactions on Information Forensics and Security, 2010, 5(1): 118-132.

[5] W. Lu, H. T. Lu and F.-L. Chung. Feature based robust water- marking using image normalization. Computers and Electrical Engineering, 2010, 36: 2-18.

[6] B. Chen, G. W. Wornell. Quantization index modulation: A class of provably good methods for digital watermarking and information embedding. IEEE Transactions on Information Theory, 2001, 47(4): 1423-1443.

[7] B. Chen, G. W. Wornell. Provably robust digital watermarking. Proceedings of the 1999 Multimedia Systems and Applications, Bellingham: Society of Photo-Optical Instrumentation Engineers, 1999, 3845: 43-53.

[8] Q. Li, I. J. Cox. Using perceptual models to improve fidelity and provide resistance to volumetric scaling for quan-tization index modulation watermarking. IEEE Transactions on Information Forensics and Security, 2007, 2(2): 127-139.

[9] Q. Li, I. J. Cox. Improved spread transform dither modulation using a perceptual model: Robustness to amplitude scaling and JPEG compression. IEEE ICASSP, 2007, 2: 185-188.

[10] X. Li, J. Liu, J. Sun, X. Yang and W. Liu. Step-projection-based spread transform dither modulation. IET Information Security, 2011, 5(3): 170-180.

[11] 张专成, 张殿富 闫小萍. 一种鲁棒的基于DWT域自适应量化步长的图像盲水印算法[J]. 中国图像图形学报, 2006, 11(6): 840-847.

[12] A. B. Watson. DCT quantization matrices visually optimized for individual images. Human Vision, Visual Processing and Digital Display IV, Bellingham, 1993, 1913: 202-216.

[13] 王颖, 肖俊, 王蕴红. 数字水印原理与技术[M]. 北京: 科学出版社, 2007.

分享
Top