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研究生(外文):Hou-Chiang Tseng
論文名稱(外文):Robust Lossless Watermarking Based on α-trimmed Mean Algorithm and Support Vector Machine
指導教授(外文):Hung-Hsu Tsai
外文關鍵詞:Support Vector MachineDiscrete Wavelet TransformLossless WatermarkingImage Authentication
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本論文植基於支向量機與α-trimmed演算法提出一強韌性影像浮水印技術,此技術是利用α-trimmed mean演算法將可能遭受污染的係數予以移除,使產生的影像特徵資訊能夠更加穩定。本技術在嵌入浮水印的過程中並未修改任何原始影像的資訊,而是利用支向量機直接記憶影像特徵浮水印與使用者簽章之間的關係。最後在驗證所有權時,只需利用已訓練的支向量機便能夠直接估計出使用者簽章。從模擬實驗證明,本論文提出技術不僅能有效抵抗常見的影像攻擊,比起以往所提出的浮水印技術有更佳的效果。因此能夠有效被運用至數位多媒體著作權保護及所有權鑑定。
The thesis presents a robust lossless watermarking technique, based on α-trimmed mean algorithm and Support Vector Machine (SVM), for image authentication. The technique does not damage the contents of original images during watermark embedding because it first trains an SVM to memorize relationship between the watermark and the image-dependent signature, and then exploits the trained SVM to estimate the watermark. Meanwhile, its robustness can be enhanced by using α-trimmed mean operator against attacks. Experimental results demonstrate that the technique not only possesses the robust ability to resist on image-manipulation attacks under consideration but also, in average, is superior to other existing methods being considered in the paper.
誌謝 i
摘要 ii
Abstract iii
表目錄 vi
圖目錄 vii
一、緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 論文架構 3
二、文獻探討 4
2.1 離散小波轉換 4
2.1.1. Haar小波轉換 4
2.1.2. Biorthogonal 9/7 wavelet 6
2.2 α-trimmed mean 演算法 10
2.3 支向量機 11
2.3.1. 線性可分支向量機(Linear separable SVM) 13
2.3.2. 線性不可分支向量機(Linear non-separable SVM) 15
2.3.3. 非線性支向量機(Non-linear SVM) 17
2.4 數位影像浮水印 19
2.4.1. 數位影像浮水印技術回顧 19
2.4.2. 數位影像浮水印特性 23
三、提出方法 25
3.1. 架構簡介 25
3.2. 問題描述 26
3.3. 浮水印產生及記憶使用者簽章 29
3.4. 抽取使用者簽章 34
3.5. 模擬實驗結果 37
3.5.1. 挑選適合實驗參數 45
3.5.2. 強韌性比較 49
3.5.3. SVM強韌性實驗 57
四、結論及未來研究方向 61
4.1 結論 61
4.2 未來研究方向 61
參考文獻 64
附錄一 Libsvm-2.8 Tool 67
附錄二 Libsvm-mat-2.85-1 Tool 73
附錄三 本論文提出方法之系統執行畫面 79
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