臺灣博碩士論文加值系統

本研究提出一個在二元影像中用於XOR運算與可變動區塊大小的高容量資訊隱藏應用方法。我們將掩護影像劃分成不重疊的母區塊，其中寬與高均設定為奇數，再將不重疊的母區塊分割成四個部分重疊的子區塊。嵌入時跳過所有全黑、全白的母區塊，我們將四個子區塊中與其他子區塊不重疊的區域的每個像素與母區塊中心像素以XOR運算來嵌入秘密資訊。一個M ×N的掩護影像可以嵌入4×m×n×M/(2m+1)×N/(2n+1)位元，其中(2m+1)與(2n+1)為分割不重疊區塊的大小。提取方法是將子區塊的各個角落與母區塊的中心像素以XOR運算得回，然後以擬亂數產生器還原秘密資訊。我們以所有可能的寬與高的組合檢驗最大嵌入量和有最少的改變率以選擇母區塊大小的寬與高值。本實驗結果說明在各掩護影像中找出最適當的區塊大小來符合具最大嵌入量或最小改變率的需求，這樣能夠減少影像的失真率，可以產生較佳的機密影像或是可以嵌入最大的秘密資訊量。

In this thesis, we propose a high capacity data hiding method applying in binary images. We divide the host image into several non-overlapping blocks as many as possible. Then we partition each block into four overlapping sub-blocks. We skip the all blacks or all whites in each block. We consider all four sub-blocks to check the XOR between the nonoverlapping parts and the center pixel of the block. The entire host image can be embedded 4×m×n×M/(2m+1)×N/(2n+1) bits. The extraction way is simply to test the XOR between center pixel with its non-overlapping part of each sub-block. All embedding bits are collected and shuffled back to the original order. The optimal means the partitioning sub-block may affect the capacities and imperception that we can reach the best. The experimental results show that the method provides the large embedding capacity and keeps imperceptible and reveal the host image lossless.

目錄
摘要 I
ABSTRACT III
致謝 V
目錄 VI
表目錄 VIII
圖目錄 IX
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 論文架構 4
第二章 文獻回顧 5
2.1 資訊隱藏 5
2.2 文獻探討 8
第三章 本文提出的方法 16
3.1 嵌入規則與方法 18
3.2 嵌入流程與演算法 22
3.3 嵌入範例 25
3.4 提取規則與方法 28
3.5 提取流程與演算法 29
3.6 提取範例 31
第四章 實驗結果 32
第五章 結論與未來工作 52
參考文獻 53


表目錄
表2-1 資訊隱藏技術的六項特點 7
表4-1 綜合圖4-2到4-9，影像大小的最大嵌入量和最小位元改變率 42
表4-2 綜合圖4-10到4-17，最大嵌入量和最小位元改變率的區塊大小 51

圖目錄
圖2-1 米開朗基羅畫作-創造亞當 6
圖3-1 掩護影像分割成不重疊的7×5區塊圖 17
圖3-2 7×5區塊可能分割成數個重疊的(m+1)×(n+1)的子區塊 18
圖3-3 全黑或全白的7×5區塊 20
圖3-4 重疊四個4×3子區塊中心和各別邊緣角落像素之位置 20
圖3-5 嵌入資訊的結構 22
圖3-6 嵌入演算法的流程圖 25
圖3-7 嵌入方法範例 26
圖3-8 假設修改中心像素的例子 27
圖3-9 提取演算法的流程圖 30
圖3-10 提取方法範例 31
圖4-1 實驗用掩護影像圖 33
圖4-2 (a)Baboon的原始影像，大小為256×256，(b)最大嵌入量為14824，(c)最小位元改變率為0.0629% 34
圖4-3 (a)Barbara的原始影像，大小為256×256，(b)最大嵌入量為9692，(c)最小位元改變率為0.0301% 35
圖4-4 (a)Calligraphy的原始影像，大小為256×256，(b)最大嵌入量為9415，(c)最小位元改變率為0.0404% 36
圖4-5 (a)Chinese newspaper的原始影像，大小為256×256，(b)最大嵌入量為10533，(c)最小位元改變率為0.0427% 37
圖4-6 (a)Green pepper的原始影像，大小為256×256，(b)最大嵌入量為6543，(c)最小位元改變率為0.0256% 38
圖4-7 (a)Mickey的原始影像，大小為256×256，(b)最大嵌入量為6397，(c)最小位元改變率為0.0233% 39
圖4-8 (a)Mountain的原始影像，大小為256×256，(b)最大嵌入量為10025，(c)最小位元改變率為0.0297% 40
圖4-9 (a)Lena的原始影像，大小為256×256，(b)最大嵌入量為8538，(c)最小位元改變率為0.0251% 41
圖4-10 (a)Baboon影像的曲線圖區塊分割大小為63×21，(b)Baboon影像的曲線圖區塊分割大小為33×33 45
圖4-11 (a)Barbara影像的曲線圖區塊分割大小為63×21，(b)Barbara影像的曲線圖區塊分割大小為33×35 46
圖4-12 (a)Calligraphy影像的曲線圖區塊分割大小為57×31，(b)Calligraphy影像的曲線圖區塊分割大小為33×3 46
圖4-13 (a)Chinese newspaper影像的曲線圖區塊分割大小為31×21，(b)Chinese newspaper影像的曲線圖區塊分割大小為33×33、33×35、35×33和35×35 47
圖4-14 (a)Green pepper影像的曲線圖區塊分割大小為61×31，(b)Green pepper影像的曲線圖區塊分割大小為3×3 48
圖4-15 (a)Mickey影像的曲線圖區塊分割大小為57×55，(b)Mickey影像的曲線圖區塊分割大小為33×33 49
圖4-16 (a)Mountain影像的曲線圖區塊分割大小為63×63，(b)Mountain影像的曲線圖區塊分割大小為33×33 49
圖4-17 (a)Lena影像的曲線圖區塊分割大小為63×63，(b)Lena影像的曲線圖區塊分割大小為33×5 50

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