臺灣博碩士論文加值系統

近年來智慧財產權、資料完整性及機密通訊越來越受到人們的重視，減少一份侵權或是仿冒品就等於是減少損失。在現今的數位時代中，由於數位影像易於複製、修改及傳送，遭受到侵權的機會也就跟著提升。資訊隱藏技術提供智慧財產權保護、資料完整性及機密通訊的有效機制。依照使用目的，最常被使用的是強韌型浮水印技術、易碎形浮水印技術及隱藏學。這些技術都是將資訊嵌入數位影像中，但其目的則不同。強韌型浮水印主要的訴求在於嵌入浮水印之影像在經過各種不同的攻擊後仍然能取回可辨識之浮水印，主要用於智慧財產權的保護；易碎形浮水印主要訴求在於透過取出之浮水印得知影像是否遭受竄改且能定位出遭受到竄改之位置，主要用於影像完整性的保護；隱藏學則是以一張影像作為掩護來傳送機密訊息，避免傳送機密訊息時被非法使用者注意到。
本論文針對影像特性在轉換域空間中提出三種資訊隱藏方法，包含強韌性浮水印方法、半易碎浮水印方法及可逆資料隱藏方法。影像之轉換域係數可代表影像的特性，不同子頻帶的係數擁有不同的強韌性，而且對影像造成的失真程度也不一樣。透過選擇不同的子頻帶係數來嵌入資訊可有效的調整強韌性及影像品質。
第三章提出一個可同時抵抗壓縮攻擊及幾何攻擊的強韌性浮水印方法。首先利用離散小波轉換(DWT)將影像轉換成DWT係數，在HL子頻帶及LH子頻帶相同索引的兩個係數被拿來產生一個梯度向量，通過調整向量的方向來嵌入浮水印，並通過增加向量的長度來改善浮水印強韌性。
第四章提出一個植基於奇異值分解(Singular Value Decomposition, SVD)及向量量化編碼法(Vector Quantization , VQ)之半易碎浮水印方法。透過VQ從SVD係數中的U矩陣及V矩陣取出影像特徵，並將這些影像特徵嵌入每一個影像子區塊的最大奇異值中。本方法的主要概念是將影像特徵嵌入到影像中，若是影像特徵或是嵌入的資訊遭受到竄改便可將受損的區域偵測出來。
第五章提出一個可逆資料隱藏方法。植基於整數小波轉換，秘密資料可被嵌入到轉換域空間，並且偽裝影像可以無損的還原回原始影像，因為人眼對於高頻區係數的修改較不敏感，所以可以改善影像品質。本方法使用係數擴展來嵌入資料，二階整數小波轉換三個高頻區的係數被使用來預測一階整數小波轉換三個高頻區係數絕對值的大小，經由這個預測可以決定在一階整數小波轉換三個高頻區係數絕對值較小的係數可嵌入一個或兩個位元，這個方法可以有效的增加嵌入的藏量。

Copyright protection, data integrity and confidential communication have been paid much more attention in recent years, and avoiding torts or counterfeits means that the loss is reduced. In today’s digital era, the chance of torts of digital images increases because digital images are easy to be copied, modified and transmitted. Information hiding is an efficient method for copyright protection, data integrity and confidential communication. The widely used techniques are robust watermarking techniques, fragile watermarking techniques and steganography according to its purpose. These techniques embed the information into digital images, but their purposes are different. The requirement of robust watermarking is that the watermark received from the watermarked image can be recognized after different kinds of attacks. It is used for copyright protection. The purpose of fragile watermarking is to determine whether the image is tampered or not and to locate the tampered region. It is used to protect image integrity. Steganography uses an image to be a camouflage to transmit the secret information, and avoids illegal users discovering the secret information.
This thesis presents three information hiding schemes in the transformation domain according to the image characteristics, including a robust watermarking, a semi-fragile watermarking and a reversible data hiding. The coefficients in the transformation domain can represent image characteristics. The robustness of the coefficients in different subbands is different, and modifying them will cause different distortions. Embedding the information into the coefficients in different subbands can effectively adjust the robustness and the image quality.
In Chapter 3, a robust watermarking scheme resisting both compression attacks and geometrical attacks is proposed. An image is transformed into discrete wavelet coefficients by using discrete wavelet transformation, and two of the coefficients having the same indices in HL subband and LH subband are used to form a gradient vector. The direction of the gradient vector is adjusted to embed the watermark, and the length is increased to improve the robustness of the watermark.
In Chapter 4, a semi-fragile watermarking scheme based on singular value decomposition (SVD) and Vector Quantization (VQ) is proposed. The SVD coefficients of U matrix and V matrix are processed by VQ to extract the image features, and these features are embedded into the biggest singular value of each image sub-block. The main idea behind the proposed scheme is to embed image features obtained from SVD coefficients, into the protected image. If image features or the embedded information are modified, the tampered regions can be detected.
In Chapter 5, a reversible data hiding scheme is proposed. Based on the integer wavelet transformation, the secret data is embedded into the transformation domain, and the stego-image can be recovered to the original image losslessly. Because the modification in the high frequency subbands is not sensitive to human vision, the image quality can be improved. The proposed scheme uses the coefficient expansion to embed the secret data. The coefficients of three 2-level high frequency subbands are used to predict the absolute value of the coefficients of three 1-level high frequency subbands. From the prediction, the small absolute value of the coefficients of three 1-level high frequency subbands can be determined to embed one secret bit or two secret bits. This scheme can increase the capacity effectively.

Abstract in Chinese I
Abstract in English III
Acknowledgements V
Contents VI
List of Tables IX
List of Figures X
Chapter 1 Introduction 1
1.1 Background 1
1.2 Image Watermarking 4
1.3 Image Steganography 7
1.4 Thesis Organization 9
Chapter 2 Preliminaries 10
2.1 Wavelet Transformation 10
2.1.1 Discrete Wavelet Transformation (DWT) 10
2.1.2 Integer Wavelet Transformation (IWT) 14
2.2 Singular Value Decomposition (SVD) 15
2.3 Vector Quantization (VQ) 15
2.4 Robust Watermarking Techniques 16
2.4.1 Digital Watermarking Using Multiresolution Wavelet Decomposition 16
2.4.1.1 Embedding Process 17
2.4.1.2 Extracting and Detecting Process 18
2.4.2 Mean Quantization Based Image Watermarking 19
2.4.2.1 Embedding Process 20
2.4.2.2 Extracting Process 21
2.5 Fragile Watermarking Techniques 22
2.5.1 An SVD and Quantization Based Semi-Fragile Watermarking Technique for Image Authentication 22
2.5.1.1 Embedding process 22
2.5.1.2 Extracting Process 23
2.5.2 Semi-Fragile Image Watermarking Method Based on Index Constrained Vector Quantization 24
2.5.2.1 Embedding Process 25
2.5.2.2 Extracting Process 26
2.6 Reversible Spread Spectrum Data Hiding 26
2.6.1 Histogram Modification 27
2.6.2 Embedding Process 27
2.6.3 Extracting process 28
Chapter 3 A Robust Grayscale Watermarking Scheme Using Gradient Vector and Angle Quantization 31
3.1 The Proposed Scheme 32
3.1.1 Embedding and Extracting Processes 32
3.1.2 Adjusting Robustness 34
3.2 Experimental Results and Discussions 35
Chapter 4 A Semi-Fragile Watermarking Scheme Based on SVD and VQ Techniques 44
4.1 The Proposed Scheme 45
4.1.1 Extracting Features 45
4.1.2 Embedding Process 46
4.1.3 Extracting the Embedded Data 47
4.1.4 Adjusting Robustness 48
4.2 Experimental Results and Discussions 50
Chapter 5 Reversible Data Hiding Based on Integer Wavelet Coefficient Expansion 56
5.1 The Proposed Scheme 56
5.1.1 Preprocess 56
5.1.2 Embedding Process 57
5.1.3 Extracting Process and Recovery 62
5.2 Experimental Results and Discussions 64
Chapter 6 Conclusions 70
6.1 Robust Watermarking Scheme 70
6.2 Semi-Fragile Watermarking Scheme 70
6.3 Reversible Data Hiding Scheme 71
References 72

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