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研究生:蘇聖鈞
研究生(外文):Sheng-Jyun Su
論文名稱:應用逆傳遞類神經網路於數位影像浮水印之研究
論文名稱(外文):Apply the Counterpropagation Neural Network to Digital Image Watermarking
指導教授:張傳育
指導教授(外文):Chuan-Yu Chang
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:電子與資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:英文
論文頁數:81
中文關鍵詞:資訊隱藏逆傳遞類神經網路數位浮水印
外文關鍵詞:digital watermarkcounter-propagation neural networkInformation hiding
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隨著數位科技的進步,數位媒體資料的散播變得相當容易,使得非法的重製和修改的情況愈來愈嚴重。數位浮水印能夠藏入特定資訊在數位媒體中,並提供對應的機制去保護及識別數位媒體。在本論文中,我們提出了一個植基於逆傳遞類神經網路架構(counterpropagation neural network)的數位影像浮水印技術,透過逆傳遞類神經網路來實現浮水印的嵌入及擷取程序。不同於傳統浮水印的方法,論文中浮水印是記憶在網路的神經鍵(synapses)中,因此在嵌入數位浮水印後的影像品質幾乎不受任何影響,也因為如此,嵌入數位浮水印後的影像遭受攻擊而造成的損壞,亦不會影像擷取出的浮水印品質。此外,透過提出的方法,我們能夠整合浮水印的嵌入及擷取程序至一逆傳遞類神經網路,並且該網路也能夠實現單一浮水印對多張影像和多浮水印對多張影像的嵌入及擷取。實驗結果顯示,透過本論文提出的浮水印方法,具有強韌性、不可查覺性和確定性。
The rapid development of computer network and multimedia technology makes it easier to assess digital media. Since the problem of illegal reproduction and modification has become more serious than before. Digital watermarks are an important technique for protection and identification that allows authentic watermarks to be hidden in multimedia. In this thesis, we propose a novel method called Full Counterpropagation Neural Network (FCPN) for digital image watermarking, in which the watermark is embedded and extracted through specific FCPN. Different from the traditional methods, the different watermarks are embedded in the synapses of a FCPN instead of the cover image. Therefore, the watermarked image is almost the same as the original cover image. In addition, most of the attacks could not degrade the quality of the extracted watermark image. Moreover, the watermark embedding procedure and extracting procedure is integrated into the proposed FCPN and it also accomplishes watermark embedding and extraction in one watermark by multi-cover image or multi- watermark by multi-cover image. The experimental results show that the proposed method is able to achieve robustness, imperceptibility and authenticity in watermarking.
Abstract in Chinese ..i
Abstract ..ii
Acknowledgment ..iv
Contents ..v
List of Figures ..vii
List of Tables ..ix
Chapter 1 Introduction..1
1.1 Motivation..1
1.2 Relative Works to Watermarking..1
1.3 Objectives..3
1.4 Thesis Outline..4
Chapter 2
Counterpropagation Neural Network ..5
2.1 Introduction..5
2.1.1 Forward-Only Counterpropagation Neural Network..6
2.1.2 Full Counterpropagation Neural Network..7
Chapter 3 Full Counterpropagation Neural Network for Watermarking..10
3.1 Introduction..10
3.2 Embedding Algorithm..14
3.3 Extracting Algorithm..15
Chapter 4 Experimental Results..16
4.1 Multiple Image Watermarking..18
4.2 Robustness test..22
4.2.1 Robustness testing for Gray-Scale Watermark..23
4.2.2 Robustness testing for Binary Watermark.. 27
4.3 Authenticity test..32
4.4 Multiple Watermark Watermarking..33
4.5 Discussions..35
Chapter 5 Conclusions..41
References ..42
Appendix Thesis in Chinese..44
[1]Fredric M. Ham and Ivica Kostanic, Principles of Neurocomputing for Science & Engineering, McGraw-Hill, Singapore, 2001, pp. 136–140
[2]R.G.van Schyndel, A.Z. Tirkel and C.F. Osborne, “A digital watermark,” in Proc. IEEE International Conf. Image Processing, 1994, vol: 2, pp. 86–92.
[3]Ren-Junn Hwang, Chuan-Ho Kao and Rong-Chi Chang, “Watermark in color image,” in Proc. First International Symposium on Cyber Worlds, 2002, pp. 225–229.
[4]Ahmidi N., Safabakhsh R., “A Novel DCT-based Approach for Secure Color Image Watermarking,” in Proc. ITCC 2004 International Conf. Information Technology: Coding and Computing, 2004, vol. 2, pp. 709–713
[5]Fengsen Deng and Bingxi Wang, “A Novel Technique for Robust Image Watermarking in the DCT Domain,” in Proc. of the International Conf. Neural Networks and Signal Processing, 2003, vol. 2, pp. 1525–1528.
[6]Jiwu Huang, YunQ.Shi and Yi Shi, “Embedding Image Watermarks in DC Components” IEEE Trans. Circuits and System for Video Technology, vol. 10, Sep. 2000, pp. 974-979
[7]Ming-Shing Hsieh, Din-Chang Tseng and Yong-Huai Huang, “Hiding digital watermarks using multiresolution wavelet transform,” IEEE Trans. Industrial Electronics, vol. 48, Oct. 2001, pp. 875 - 882
[8]Chiou-Ting Hsu and Ja-Ling Wu, “Hidden digital watermarks in images,” IEEE Trans. Image Processing, 1999, vol. 8, pp. 58-68
[9]K.J Davis and K. Najarian, “Maximizing Strength of Digital Watermarks Using Neural Networks,” in Proc. International Joint Conf. Neural Networks, 2001, vol. 4, pp.2893–2898.
[10]Shi-chun Mei, Ren-hong Li, H. Dang and Yun-kuan Wang, “Decision of image watermarking strength based on artificial neural-networks,” in Proc. 9th International Conf. Neural Information Processing, 2002, vol. 5, pp. 2430–2434.
[11]Zhang Zhi-Ming, Li Rong-Yan, Wang Lei, “Adaptive Watermark Scheme with RBF Neural Networks,” in Proc. International Conf. Neural Networks and Signal Processing, 2003, vol. 2, pp. 1517–1520
[12]R. Hecht-Nielsen, “Counterpropagation Networks,” in IEEE Proceedings of International Conference on Neural Network, 1987, vol. 2, pp. 19-32
[13]Liu Jun and Wang Danwei, “Data Compression For Image Recognition,” in International Joint Conference on Neural Networks , 1992, vol. 4, pp. 333-338
[14]Zafar M.F and Mohamad D, “Counterpropagation neural networks for trademark recognition,” in International Symposium on Signal Processing and its Applications, 2001, vol. 2, pp. 751-752
[15]Chang, W., Soliman H.S. and Sung A.H., “Image data compression using counterpropagation network,” in IEEE International Conference on Systems, Man and Cybernetics, 1992., vol. 1, pp. 405-409
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