跳到主要內容

臺灣博碩士論文加值系統

(3.236.23.193) 您好!臺灣時間:2021/07/24 13:51
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:洪振榮
研究生(外文):Chen-Jung Hong
論文名稱:基於特徵點之數位影像版權保護
論文名稱(外文):Copyright Protection for Digital Images Using Feature Points
指導教授:孫宏民
指導教授(外文):Hung-Min Sun
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
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:2004
畢業學年度:92
語文別:英文
論文頁數:68
中文關鍵詞:數位影像版權保護特徵點特徵擷取幾何攻擊Marr wavelet
外文關鍵詞:Digital image copyright protectionfeature pointsFeature extractiongeometric distortionMarr wavelet
相關次數:
  • 被引用被引用:0
  • 點閱點閱:119
  • 評分評分:
  • 下載下載:17
  • 收藏至我的研究室書目清單書目收藏:2
傳統的浮水印技術面臨兩個重要的問題。第一,在演算公開的情況下,很難抵擋針對演算法所設計出來的攻擊,這些攻擊可以輕易地抹除已加入的浮水印,第二,一張影像可以同時加入很多浮水印,甲說這是他的影像,乙說這也是他的影像,兩者都可以萃取出浮他們的浮水印,然而我們就無法得知誰這張影像的所有權歸誰所有。這兩個問題,使浮水印的技術無法全面的發展。
數位影像版權保護有別於傳統的浮水印技術,最重的的特點是對於被保護的影像,我們無須修改它。我們只要將影像的特徵取出,然後用萃取出的特徵再向授權的公正的第三者註冊,再完成註冊手續的同時,一個時間戳記被紀錄,用來證明註冊的時間。將來如果對影像的所有權有爭論,第一個註冊的人將被公定為合法的所有權人。
對於影像的特徵的翠取,有很多方法提出來。但很少特徵的翠取方法,在受到各式各樣的攻擊依然保持不變,尤其是受到幾何上的攻擊。我們的方法是基於Mexican Hat Wavelet scale interaction 所萃取出來的特徵點。這種方法萃取出來的特徵點可以抵擋訊號處理攻擊和幾何攻擊。
查此張影像是否已經註過冊,我們就要拿這張影像萃取出來的特徵,再儲存資料庫逐一比對是否吻合。要注意的是,這張影像可能已遭受到攻擊,特徵點可能有些位置上的改變。我們觀察到,這些特徵點有結構性的位移變化,換句話說,特徵點之間的相對位置並沒有明顯的改變。因此在我們提出的方法中,提出一系列的反運算以訂正受到扭曲變化的特徵點。訂正過後,再來比對是否吻合。
Traditional watermark scheme has encountered two serious problems. First, it is easy to attack watermark by methods which are specifically designed for its algorithm. Second, an image may be embedded many watermark simultaneously. We have no ideal which watermark was embedded first which one was embedded latter. Therefore, to determine truly owner of the content becomes impossible.
Digital image copyright protection scheme is very different from traditional watermark. No modification of original image is needed. Only what we should do is extract feature of image and register to the third party with it. At the same time, we get a time stamp to prove at what time we register the content. When argument happened, the first registrant is truly legal owner of the image. The first registrant is defined who has the earliest time stamp.
There are many ways proposed for feature extraction. But seldom feature remains unchanged under various attacks, especially geometric attacks. Our feature extraction method is called Mexican Hat Wavelet scale interaction. The extracted feature points can resist both geometric distortion and signal processing.
Here come the problems about feature points pattern matching when we want to check an image registered or not. We observe that feature points have structural position shift. So our methods applied many inverse transformations to correct its distortion when suffered from attacks.
Chapter 1 Introduction
1.1 Motivation
1.2 Traditional Watermark
1.3 Digital Image Copyright Protection Technique
1.4 Attacks
1.5 Possible Approaches
1.6 Structure of the Paper
Chapter 2 Previous Researches
2.1 Digital Image Copyright Protection on Spatial Domain
2.2 Digital Image Copyright Protection on Frequency Domain
Chapter 3 Our Scheme for Image Copyright Protection
3.1 Introduction to Feature Points Extraction
3.2 The Proposed Algorithm
3.2.1 Register Phase
3.2.2 Detect Phase
3.2.3 Security Analysis
Chapter 4 . Simulation
Chapter 5 . Conclusions
References
[1] C. C. Chang, K. F. Hwang, and M. S. Hwang. “A block based digital watermarks for copy protection of images,” In Proceedings of APCC/OECC’99, pages 977-980, Beijing, October 1999.
[2] G. Voyatzis and I. Pitas. “Chaotic mixing of digital images and applications to watermarking. Proceedings of European Conference on Multimedia Applications,” Services and Techniques (ECMAST ‘96), 2:687-695.
[3] S. Craver, N. Memon, B. L. Yeo, and M. Yeung, “Can Invisible Watermarks Resolve Rightful Ownership?” Proc. SPIE Storage and Retrieval for Still Image and Video Databases V, Vol. 3022, 1997, pp. 310-321.
[4] W. B. Lee, and T. H. Chen, "A Robust Copyright Protection Scheme for Still Images," Proceedings of 2000 International Computer Symposium Workshop, Dec. 2000,
[5] C. Y. Lin, M. Wu, J. A. Bloom, I. J. Cox, M. L. Miller, and Y. M. Lui, “Rotation, Scale and translation resilient public watermarking for images,” Proc. SPIE Security Watermarking Multimedia Contents II, vol.3971, pp. 90-98,2000
[6] S. Pereira, J. J. K. ÓRuanaidh, and F. Deguillaume, “Template based recovery of Fourier-based watermarks using log-polar and log-log maps,” in Proc. IEEE Int. Conf. Multimedia Comput. Syst., vol. 1, 1999,, pp.870-874
[7] Shelby Pereira, and Thierry Pun, Member, IEEE, “Robust Template Matching for Affine Resistant Image Watermarks,” IEEE transactions on image processing, VOL. 9 NO.6,June 2000.
[8] Z. Ni, E. Sung, and Y. Q. Shi, “Enhancing robustness of digital watermarking against geometric attack based on fractal transform,” in Proc. IEEE Int. Conf. Multimedia Expo., vol. 2,2000,pp. 1033-1036.
[9] M. Gruber and K. Y. Hsu, “Moment-based image normalization with high noise-tolerance,” IEEE Trans. Pattern Annual. Machine Intell., vol. 19, pp.136-139, Feb. 1997.
[10] M. Alghomiemy and A. H. Tewfik, “geometric distortion correction through image normalization,” in Proc. IEEE Int, Conf. Multimedia Expo., vol. 3, 2000, pp. 1291-1294.
[11] M. Alghomiemy and A. H. Tewfik, “Image watermarking by moment invariants,” in Proc. IEEE Int. Conf. Image Process., vol. 2, Jan. 2001, pp. 73-76.
[12] A. Nikolaidis and I. Pitas, “Robust watermarking of facial Images based on salient geometric patter matching,” IEEE Trans. Multimedia, vol. 2, pp. 172-184, Sept. 2000.
[13] M. Kutter, S. K. Bhattacharjee, and T. Ebrahimi, “Toward second generation watermarking schemes,” in Proc. IEEE Int. Conf. Image Process., vol. 1, 1999, pp. 320-323.
[14] P. Bas, J.-M. Chassery, and B. Macq, “Robust watermarking based on the warping of Multimedia Contents II, vol. 3971, pp.99-109, 2000.
[15] Sushil Bhattacharjee, and Martin Kutter, “Compression Tolerant Image Authentication,” in Proc. IEEE Int. Conf. Image Process., vol. 1,1998,pp.435-439
[16] B. S. Manjunath, C. Shekhar, and R. Chellappa, “A new approach to image feature detection with applications,” Pattern Recogn., vol. 29, no. 4, pp,627-640, 1996.
[17] Chih-Wei Tang, and Hwueh-Min Hang, Fellow, IEEE, “A Feature-Based Robust Digital Image Watermarking Scheme,” IEEE transactions on signal processing, vol. 51, NO. 4,April 2003.
[18] Martin Garvrilov, Piotr Indyk, Rajeev Motwanim, and Wuresh Venkatasubramanian, “Combinatorial and Experimental Methods for Approximate Point Patter Matching.”
[19] 孫宏民,陳孟彰,“無失真數位浮水印技術“,In Proceedings of the Twelfth National Conference on Information Security, page 25-32, 2002.
[20] 陳同孝,董俊良,白明弘,“植基於離散餘弦轉換之影像權益保障系統“,In Proceedings of the Ninth National Conference on Information Security, page 1-7, 2001.
[21] 陳同孝,吳曉蓉,“植基於向量量化編號法之影像權益保障系統“,In Proceedings of the Ninth National Conference on Information Security, page 75-81, 2001.
[22] 張真誠,黃國峰,陳同孝,“電子影像技術“,松崗電腦圖書資料股份有限公司,2000.
[23] 陳同孝,張真誠,黃國峰,“數位影像處理技術“,松崗電腦圖書資料股份有限公司,2001.
[24] Stirmark. [Online]. Available :
http://www.cl.cam.ca.uk/~fapp2/watermarking/stirmark/
[25].孫宏民,陳孟彰,“無失真數位浮水印技術“,In Proceedings of the Twelfth National Conference on Inform
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top