臺灣博碩士論文加值系統

在現今的數位生活中，數位浮水印技術已經廣泛被人們使用於版權保護上。數位浮水印技術的做法，是將一些特定的數位訊號嵌入在受保護的媒體中，受保護的媒體可以是影片、聲音、圖片等。在取出時，使用者利用相同的演算法，將浮水印資訊取出，如此一來就可以證明受保護媒體的合法擁有者。而為了達到有效的版權保護，在嵌入演算法上面必須達到強韌性，即是保護影像受到各種攻擊的狀況下，仍然能從攻擊過後的保護圖片，取出嵌入的浮水印，進一步證明版權的擁有者。
本論文針對影像中顯著的特徵在空間域和轉換域裡提出兩種強韌性浮水印技術。在第三章提出一個在空間域裡可抵抗幾何攻擊的強韌型浮水印技術。主要使用角偵測方法尋找出來被保護影像中重要的邊緣特徵。這些邊緣特徵，通常在圖像中都是強韌度較高，不容易遭受到破壞。因此即使再經過旋轉、放大、幾何攻擊，邊緣特徵位置還是可藉由角偵測方式順利取出。由於這些邊緣特徵都是藉由角偵測方式選取，即使經過幾何攻擊後，依然可以順利的取出嵌入資訊。
在第四章提出一個植基於奇異值分解(Singular Value Decomposition , SVD)及粒子群演算法(Particle Swarm Optimization, PSO)之有效強韌型浮水印方法。主要是透過轉換域下的奇異值分解來擷取保護影像中重要的特徵，再利用粒子群演算法去修改這些重要特徵係數，來達到嵌入浮水印的目地。由於SVD的特徵值矩陣，在經過遞移、轉置、旋轉攻擊等，都可以維持不變，因此藉由SVD的特性，所提出的浮水印技術是具備有強韌性。最後從實驗結果可以清楚了解本論文提出嵌入浮水印技術，對於抵抗攻擊是有效的。

Nowadays, our life is full of the digital technologies, and the digital watermarking has been used for copyright protection widely. The methods of digital watermarking technique are to embed the specific signal in the host media that we want to protect. The media could be video, voice or image…etc. In extraction procedure, users use the algorithm which same as what provider embedded in to extract the watermark information. Therefore, users can prove the legal ownership by utilizing the watermark information. To achieve workable copyright protection, have to keep the embedded algorithm robust enough. That is, users can still extract the watermark information to provide the legal ownership even the media had been attacked or affected under any circumstance.
This thesis presents two robust watermarking schemes in the spatial domain and transform domain according to the image signification features. In Chapter 3, a robust watermarking scheme that resisting geometric attack is proposed. The major method uses the corner detection to find the important edge features from the host protected image. These edge features usually are more robust and not being damaged easily. Therefore, the location information of edge features still can be recovered even the image has been rotated, scaled, and geometric attacked. Since these edge features are selected by corner detection, the embedded information can still be extracted under the geometric attack. Since these edge features are selected by corner detection, the embedded information can still be extracted under the geometric attack.
In Chapter 4, an effective robust watermarking scheme which based on singular value decomposition (SVD) and particle swarm optimization (PSO) is proposed. The core idea aims at the transform domain method which is the singular value decomposition. The SVD can extract important feature of protected image and then we use PSO algorithm to modify the singular values for embedding. Because the singular value matrix of SVD remains unchanged via translation attack, transpose attack, rotation attack, and the proposed watermarking technique is robust based on the properties of SVD. Finally, we clearly demonstrate that the watermarking technique to against attacks is workable. Besides, does not need to record the extra information because of the quantization method.

Contents
Abstract in Chinese I
Abstract in English III
Contents VII
List of Tables X
List of Figures XI
Chapter 1 Introduction 1
1.1 Background 1
1.2 Image Watermarking 2
1.3 Research Motivation 5
1.4 Thesis Organization 7
Chapter 2 Preliminaries 8
2.1 Harris Corner Detector 8
2.2 Sobel Edge Detection 10
2.3 Singular Value Decomposition (SVD) 11
2.4 Particle Swarm Optimization (PSO) 12
2.5 Robust Watermarking Schemes 14
2.5.1 A Feature-Based Robust Digital Image Watermarking Against Geometric Attacks 14
2.5.1.1 Watermarking Embedding Algorithm 15
2.5.1.2 Watermarking detection Algorithm 15
2.5.2 A New Robust Reference Watermarking Scheme Based on DWT-SVD 16
2.5.2.1 Watermarking Embedding Process 16
2.5.2.2 Watermarking Extraction Process 17
Chapter 3 A Robust Watermarking Using Harris Corner Detector and Quantization Technique 19
3.1 The Proposed Scheme 19
3.2 Preprocessing 20
3.3 Watermarking Embedding Algorithm 21
3.4 Watermarking Extraction Algorithm 25
3.5 Experimental Results and Discussions 26
3.5.1 Performance Analysis 27
Chapter 4 A Robust Watermarking Scheme based on Singular Value Decomposition and Particle Swarm Optimization Algorithm 32
4.1 The Proposed Scheme 32
4.2 Watermarking Embedding Algorithm 33
4.3 Modification Optimized Range of Embedding 37
4.4 Watermarking Extraction Algorithm 39
4.5 Experimental Results and Discussions 40
4.5.1 Performance Analysis 41
4.5.2 Comparisons with Existing Techniques 49
Chapter 5 Conclusions and Future Works 51
5.1 Conclusions 51
5.2 Future Works 52
Reference 53

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