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研究生:蔡仁勝
研究生(外文):Jen-ShengTsai
論文名稱:應用於多媒體安全之以特徵為基礎的數位浮水印研究
論文名稱(外文):Feature-Based Digital Watermarking for Multimedia Security
指導教授:郭耀煌郭耀煌引用關係
指導教授(外文):Yau-Hwang Kuo
學位類別:博士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:2012
畢業學年度:100
語文別:英文
論文頁數:101
中文關鍵詞:多媒體安全影像浮水印三維網格浮水印特徵偵測區域選擇
外文關鍵詞:Multimedia SecurityImage Watermarking3D Mesh WatermarkingFeature DetectionRegion Selection
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隨著數位多媒體資料持續增加與廣泛應用,多媒體安全在保護其內容免於非法使用變得愈來愈重要。數位浮水印是應用於多媒體安全上處理這些議題的核心技術,例如版權保護、內容認證和竄改偵測。近來很多針對這些目標的浮水印研究被提出,但是在實際應用仍然存在著一些困難,包含強健性與安全性等。因此,本論文針對多媒體安全提出一個新穎的基於特徵之數位浮水印架構和其相對應的基本設計原則,藉著同時考量多媒體內容屬性和浮水印需要性以偵測出適當的特徵,接著根據特徵值挑選出最好的區域集合,與浮水印資訊結合達到最佳的效能。基於前述所提的架構,我們將其具體實現於二維影像和三維網格浮水印方法以驗證效能。在二維影像浮水印方法中,我們利用尺度適應性自相關矩陣和高斯拉普拉斯運算從目標影像中獲得特徵區域,並且建構一個選擇程序評估特徵區域的屬性,用以決定一個最佳的區域集合來嵌入浮水印的資訊,它可以針對各種攻擊達到最大的強健性,並且可以盡量保有影像本身的品質,我們也在偵測與選取特徵區域的程序加入隨機性來強化安全性,用以防止浮水印資訊被未授權的惡意使用者任意的存取。在三維網格浮水印方法中,首先從目標網格模型中偵測出可抵抗姿勢改變的特徵,包含三維的表面距離和形狀直徑特徵值,接著建構特徵的直方圖以結合浮水印資訊,達到在保有一定的強健性下具有最小的失真度。最後,我們以充分的實驗與分析結果具體呈現所提出的架構具可行性及有效性。
With the proliferation of digital multimedia data, multimedia security has become increasingly important to protect the content from illegal uses. Digital watermarking is a core enabling technology for multimedia security to deal with the issues in multimedia applications, such as copyright protection, content authentication and tampering detection. Recently, much research in this technology has been proposed for the purpose, but there are still some challenging difficulties for achieving robustness and security. In this dissertation, a new feature-based watermarking framework and its fundamental design principles are presented to mitigate the difficulties. We begin with detecting good features by jointly considering multimedia properties and watermarking requirements. An optimal region set is selected according to the detected features and linked with watermark information to achieve a desired goal. The framework is implemented in two-dimensional (2D) image and three-dimensional (3D) mesh watermarking to verify its performance. In the 2D image watermarking, local features are obtained from a target image based on the scale-adapted auto-correction matrix and the Laplacian-of-Gaussian operation. A selection process is then constructed to choose a feature region set, which has the greatest robustness against various attacks and can preserve image quality as much as possible after being watermarked. Moreover, we incorporate randomization in determining features to mitigate the leakage of secret information for enhancing watermarking security. In the 3D mesh watermarking, pose-oblivious features that include the geodesic distance and the local shape diameter are detected from a target mesh model. We build histograms of these features for watermarking to minimize mesh distortions while keeping robustness. Extensive experiments and analyses are conducted to clearly demonstrate the effectiveness of our proposed framework.
Chapter 1. Introduction 1
Chapter 2. Feature-Based Digital Watermarking Framework 11
Chapter 3. On the Selection of Optimal Feature Region Set for Robust Digital Image Watermarking 19
3.1. Selection of Optimal Feature Region Set Based on Simulated Attacking and MDKP Techniques 20
3.1.1. Primary Feature Set Searching Stage 21
3.1.2. Feature Set Extension Stage 25
3.2. Experimental Results and Discussions 30
3.2.1. False-Positive Analysis 31
3.2.2. Evaluation of Robustness 34
3.2.3. Discussions 36
Chapter 4. Joint Robustness and Security Enhancement for Feature-Based Image Watermarking Using Invariant Feature Regions 41
4.1. Robust and Secure Image Features for Watermarking 43
4.1.1. Detection of Robust and Secure Features 43
4.1.2. Feature Region Selection 46
4.2. Proposed Watermark Embedding and Detection Schemes 49
4.2.1. Watermark Embedding Scheme 50
4.2.2. Watermark Detection Scheme 54
4.3. Experimental Results 56
4.3.1. Evaluation of Robustness 56
4.3.2. Security Analysis 68
4.3.3. Discussions 72
Chapter 5. Pose-Oblivious Watermarking Method for Three-Dimensional Mesh Model 75
5.1. Pose-Oblivious Features of 3D Mesh Model 75
5.1.1. Geodesic Distance Based Pose-Oblivious Features 76
5.1.2. Local Shape Diameter Based Pose-Oblivious Features 78
5.2. Pose-Oblivious Watermark Embedding and Detection Schemes 79
5.2.1. Histogram Construction 79
5.2.2. Watermark Insertion 80
5.2.3. Watermark Extraction 81
5.3. Experimental Results and Discussions 82
5.3.1. Distortion Evaluation 83
5.3.2. Robustness Evaluation 84
Chapter 6. Conclusions and Future Work 90
References 93
Author’s Publications 100

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