臺灣博碩士論文加值系統

隨著網際網路的快速普及，人們可以輕易地互相傳送、獲取資訊，無形中也增加了資訊在傳送時被不法第三者攔截的機會，因而突顯了資訊安全的重要性。尤其對軍事及醫學領域而言，任何的誤差都是不允許的，為了達成此目的，無失真資訊隱藏便成為目前相當熱門的研究議題。
根據以上的動機，本篇論文首先提出一個有效的無失真資訊隱藏技術，隱蔽影像經由快速量化產生剩餘空間以嵌入機密資訊。提出之方法應用鄰近像素的複雜度分析預測機密資訊的位元個數，並藏入到像素中。換言之，將會保存原圖和量化圖的差異值，以完全還原隱蔽影像。根據實驗的結果，以一張標準Lena而言，所提方法的資訊嵌入量為0.9 BPP，而Maniccam和Bourbakis的方法只有0.3 BPP。
此外，截至目前為止已有相當多的無失真資訊隱藏技術提出，例如誤差擴張、整數函式轉換、直方圖修改法等，其中直方圖修改法以修改像素直方圖中最大值方式將機密訊息藏入影像中，此法的影像品質相當不錯。然而，直方圖修改法的資訊藏量不高，為了解決這個問題，本篇論文以區塊內外部預測方式提出一個無失真資訊隱藏方法，以區塊內外之誤差為藏入點並應用直方圖修改法將訊息藏入。由實驗結果可知，所提方法不但能明顯的提昇資訊藏量，並且維持一定的影像品質。

Along with Internet fast popularization, people can easily share and obtain information each other. It also imperceptibly increases the probability of intercept by the illegal third party in the information transmission. Therefore, it appears the importance of information security, especially in the military or the medicine image that does not allow having any error. In order to achieve this goal, the lossless information hiding property is an important subject.
According to the motivation above, we first propose an effective lossless information hiding scheme, in which a host image is quantized firstly to generate spare spaces for hiding secret messages. The proposed scheme applies the complexity analysis of neighboring pixels to predict the number of secret message bits concealed in a pixel. In other words, the scheme reserves the differences between the host image and the quantized image for completely restoring the host image. According to the experimental results, the information capacity of the proposed scheme is 0.9 BPP for the standard Lena while that of Maniccam and Bourbakis’s scheme is only 0.3 BPP.
In addition, there are many proposed lossless information hiding techniques such as difference expansion, integer transformation method, histogram modification and so on. Among them, histogram modification modifies maximum pixel value in a histogram to embed secret messages into a host image. The quality of stego image generated by the histogram modification is good. However, the capacity of histogram modification method is low. Therefore, we propose a lossless information hiding scheme to improve this problem in this thesis by using black-based internal and external forecasting. According to the experimental results, the proposed scheme can increase the information capacity and maintain image quality.

摘要 I
Abstract II
誌謝 IV
目錄 V
表目錄 VII
圖目錄 VIII
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 1
1.3 論文架構 2
第二章 文獻探討 3
2.1 資訊隱藏 3
2.2 最低位元法(Least Significant Bit, LSB) 4
2.3 算數編碼(ARITHMETIC CODING) 6
2.4 植基於直方圖操作 8
2.4.1 Ni等學者所提之方法 8
2.4.2 Hsieh等學者所提之方法 12
2.4.3 Yeh等學者所提之方法 13
第三章 植基於複雜度分析之無失真資訊隱藏技術 16
3.1 研究方法 16
3.2 嵌入及取出過程 18
3.2.1 嵌入過程 19
3.2.2 取出過程 21
3.3 實驗結果 23
第四章 植基於直方圖預測編碼之無失真資訊隱藏方法 29
4.1 多重預測編碼之方法 29
4.2 嵌入演算法 30
4.2.1 外部預測編碼之嵌入 30
4.2.2 內部預測編碼之嵌入 33
4.3 取出演算法 34
4.3.1 內部預測編碼之回復 34
4.3.2 外部預測編碼之回復 36
4.4 實驗結果 38
第五章 結論與未來工作 42
5.1 結論 42
5.2 未來工作 42
參考文獻 43

表目錄
表2.1 符號的機率分佈與區間 7
表3.1 嵌入資訊的格式(W) 19
表3.2 四張影像壓縮過後的位元 24
表3.3 Maniccam和Bourbakis 提出方法的嵌入量及所提方法之比較 25
表4.1 所提方法與其他學者比較藏入負載量與影像品質 40

圖目錄
圖2.1 資訊隱藏分類圖 3
圖2.2 算術編碼之示意圖 7
圖2.3 Ni等學者所提方法之嵌入範例 10
圖2.4 Ni等學者所提方法之最高點與無零點時嵌入過程 11
圖2.5 Ni等學者所提之取出範例 12
圖2.6 Yeh等學者所提方法之隱蔽影像 13
圖3.1 藏入點位置 17
圖3.2 像素x值的8個鄰近像素 17
圖3.3 案例圖形 18
圖3.4 門檻值與嵌入位元 18
圖3.5 複雜度分析 26
圖3.6 不同位元個數所嵌入的位置 26
圖3.7 要嵌入的機密影像 27
圖3.8 原圖和嵌入後的影像 27
圖3.9 所提方法與Tian及Alattar方法之比較圖 28
圖4.1 所提方法以4×4區塊進行預測之示意圖 30
圖4.2 所提方法4×4區塊進行預測案例 31
圖4.3 所提方法4×4區塊位移後之直方圖 31
圖4.4 所提方法4×4區塊藏入後之外部預測值與直方圖 32
圖4.5 所提方法4×4區塊之外部預測藏入後返回預測 32
圖4.6 所提方法內部區塊預測後之結果與直方圖 33
圖4.7 所提方法4×4區塊之內部預測藏入後返回預測 34
圖4.8 所提方法4×4區塊之內部預測與直方圖 35
圖4.9 所提方法4×4區塊之內部預測返回示意圖 35
圖4.10 所提方法4×4區塊外部預測示意圖與直方圖 36
圖4.11 所提方法外部取回與位移後之示意圖與直方圖 37
圖4.12 所提方法4×4區塊之外部預測返回示意圖 37
圖4.13 外部與內部預測誤差值之直方圖 39
圖4.14 嵌入後偽裝影像 41

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