臺灣博碩士論文加值系統

在這個資訊化的時代，人們可以快速地由網路取得大量的數位影像資訊，但在未經授權下任意的竄改已經嚴重危害到數位資訊擁有者的合法權益，保護智慧財產權變成一項重要的課題，因此人們採取了以影像浮水印來解決智慧財產權爭議的問題。
轉換域的數位浮水印技術是先以各種不同的轉換公式擷取影像特徵，再將數位浮水印資訊嵌入至這些特徵參數中。本篇論文提出以離散小波轉換(Discrete Wavelet Transform, DWT)結合奇異值分解(Singular value decomposition, SVD)和離散餘弦轉換離散餘弦轉換(Discrete Cosine Transform, DCT)等技術所架構出的新穎數位浮水印技術，其中DWT、SVD、DCT等三種轉換各有專擅之處，DWT可解析低頻的影像，SVD可擷取各層的幾何紋理，DCT則用來分析特定頻率成份。本文先找出複合式之DWT-SVD-DCT轉換域的特徵參數後使用量化索引調變(Quantization Index Modulation, QIM)嵌入浮水印，其後與其它類似的浮水印技術相互比較以印證成效。
實驗中，我們試著將64x64的二元浮水印圖像嵌入五個512x512的灰階影像，所提出的DWT-SVD-DCT複合域嵌入法所導致的PSNR值都高於42dB，這顯示肉眼已幾乎無法分辨影像浮水印與原始影像之差別；本法對於各類攻擊的抵抗能力也非常優秀，即令在高比例的壓縮攻擊下（如JPEG壓縮\QF=20），所抽取出之浮水印的NC平均值仍高達0.889，其對於高斯雜訊、椒鹽雜訊、濾波、旋轉、剪裁、亮度調整等攻擊也可以有效地抵抗。

In the information age, a large amount of digital image information can be acquired from the Internet. However, any unauthorized tampering may seriously jeopardize the legitimate interests of the digital information owner, and the protection of intellectual property rights has become an important issue. Hence, image watermarking is used to resolve the intellectual property contentious issues.
For many transform-domain watermarking techniques, attempts have been made to exploit various transformations to extract image features, and then embed the watermark into those feature parameters. This study used an innovative digital watermarking technology constructed by integrating the discrete wavelet transform (DWT), singular value decomposition (SVD) and discrete cosine transform (DCT). The transformation technologies of DWT, SVD, and DCT have their respective advantages. DWT can resolve the low frequency image, SVD can capture the geometric textures of various layers, and DCT can be used to analyze the components of specific frequencies. This study first identified the feature parameters of the composite DWT-SVD-DCT domain, and then used the quantization index modulation (QIM) rule to embed the watermark. To verify the effect of the proposed scheme, the resulting performance were compared with five other watermarking technologies.
The performances of the watermarking technologies will evaluated by embedding a 64x64 binary watermark into five 512x512 greyscale images. The results showed that the proposed scheme can result in a PSNR value up to 42 dB after watermark embedding. For a watermarked image with a PSNR greater than 40dB, the differences between the watermarked and original image are almost undetectable by human eyes. The proposed scheme also exhibited satisfactory robustness against common digital signal processing attacks. Even under the high-intensity attack of JPEG compression (e.g., quality factor = 20), the average mean of the normalized correlation (NC) values of the recovered watermarks still reached 0.889. Moreover, the proposed method can effectively resist the general attacks by Gaussian noise, salt and pepper noise, filtering, rotation, cropping, and brightness adjustment.

目錄
摘要 I
Abstract II
誌謝 IV
目錄 V
表目錄 IX
圖目錄 XI
Chapter 1 緒論 1
1-1 研究背景與動機 1
1-2 浮水印之特性 1
1-3 浮水印架構介紹 5
1-3-1 浮水印的嵌入、提取與驗證 5
1-3-2 浮水印嵌入方式的種類 6
1-3-3 浮水印強韌度需求的種類 9
1-3-4 浮水印提取方式的種類 9
1-4 問題解決策略 10
1-5 論文架構 11
Chapter 2 背景知識 13
2-1 頻率域 13
2-1-1 離散小波轉換(Discrete Wavelet Transform, DWT) 13
2-1-2 離散餘弦轉換(Discrete Cosine Transform, DCT) 16
2-2 奇異值分解(Singular Value Decomposition, SVD) 19
2-3 中國餘數定理(Chinese Remainder Theorem, CRT) 20
2-4 Arnold transform 22
Chapter 3 與DWT、DCT、SVD相關之影像浮水印技術 23
3-1 以DCT為基礎之數位浮水印方法 24
3-1-1 文獻[16]嵌入與抽取流程 24
3-1-2 文獻[17]嵌入與抽取流程 27
3-2 以DWT與SVD為基礎之數位浮水印方法 30
3-2-1 文獻[20]嵌入與抽取流程 30
3-2-2 文獻[21]嵌入與抽取流程 33
3-3 以DCT與SVD為基礎之數位浮水印方法 36
3-3-1 文獻[22]嵌入與抽取流程 36
Chapter 4 基於DWT-SVD-DCT的數位浮水印嵌入技術 40
4-1 前言 40
4-2 提取特徵參數 41
4-3 DWT-SVD-DCT的數位浮水印嵌入技術 43
4-4 DWT-SVD-DCT的提取技術 46
Chapter 5 實驗結果與分析 48
5-1 各種影像浮水印的PSNR數值 50
5-2 實驗結果的比較-Lena 51
5-3 實驗結果的比較-Peppers 52
5-4 實驗結果的比較-Baboon 53
5-5 實驗結果的比較-Airplane 54
5-6 實驗結果的比較-Boat 55
5-7 實驗結果討論 57
5-8 DWT-SVD-DCT技術的延伸 59
Chapter 6 結論 62
參考文獻 63

表目錄
表 1 1 可視數位浮水印與不可視數位浮水印之比較 3
表 1 2 數位浮水印的分類 10
表 3 1 相關文獻的分類與優劣性 23
表 5 1 五種影像浮水印的PSNR數值(最佳數值-紅粗體、次佳數值-黑粗體) 50
表 5 2 各種影像攻擊後的NC數值-Lena(最佳數值-紅粗體、次佳數值-黑粗體) 51
表 5 3 各種影像攻擊後的NC數值-Peppers(最佳數值-紅粗體、次佳數值-黑粗體) 52
表 5 4 各種影像攻擊後的NC數值-Baboon(最佳數值-紅粗體、次佳數值-黑粗體) 53
表 5 5 各種影像攻擊後的NC數值-Airplane(最佳數值-紅粗體、次佳數值-黑粗體) 54
表 5 6 各種影像攻擊後的NC數值-Boat(最佳數值-紅粗體、次佳數值-黑粗體) 55
表 5 7 全部攻擊結果的NC數值之平均值(最佳數值-紅粗體、次佳數值-黑粗體) 56
表 5 8 各種影像攻擊後的雙浮水印NC數值 61

圖目錄
圖 1 1 浮水印的分類 (a)可視數位浮水印；(b)不可視數位浮水印 2
圖 1 2 數位影像浮水印嵌入流程圖 5
圖 1 3 數位影像浮水印提取流程圖 6
圖 1 4 空間域嵌入示意圖 7
圖 1 5 頻率域嵌入示意圖 8
圖 2 1 離散小波轉換 (a)原始影像係數；(b)水平分割結果 14
圖 2 2 離散小波轉換 (a)垂直分割結果；(b)一階DWT的四個子頻帶 15
圖 2 3 多階二維離散小波轉換的子頻帶分佈圖 16
圖 2 4 DCT的頻率分佈圖 18
圖 3 1 Zigzag掃描 24
圖 3 2 文獻[16]嵌入流程圖 26
圖 3 3 文獻[17]嵌入流程圖 29
圖 3 4 文獻[20]嵌入流程圖 32
圖 3 5 文獻[21]嵌入流程圖 35
圖 3 6 文獻[22]嵌入流程圖 38
圖 4 1 使用Arnold transform將數位浮水印打散 41
圖 4 2 特徵參數的提取與嵌入流程圖 45
圖 4 3 數位浮水印嵌入結果 (a)Lena之原始影像；(b)Lena之影像浮水印 46
圖 4 4 數位浮水印嵌入結果 (a)Lena之影像浮水印；(b)嵌入後提取之數位浮水印 47
圖 5 1 原始影像與數位浮水印 48
圖 5 2 遭受攻擊後提取出的數位浮水印 49
圖 5 3 JPEG壓縮\QF=20 的浮水印-Lena 51
圖 5 4 JPEG壓縮\QF=20 的浮水印-Peppers 52
圖 5 5 JPEG壓縮\QF=20 的浮水印-Baboon 53
圖 5 6 JPEG壓縮\QF=20 的浮水印-Airplane 54
圖 5 7 JPEG壓縮\QF=20 的浮水印-Boat 55
圖 5 8 雙浮水印之嵌入結果 (a)嵌入之雙浮水印；(b)雙浮水印嵌入後之影像浮水印 60

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