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研究生:劉騏鋕
研究生(外文):Chi-Chih Liu
論文名稱:植基於人類視覺系統、類神經網路及支向量機之強靭影像浮水印
論文名稱(外文):Robust Image Watermarking Using Human Visual System, Neural Networks and Support Vector Machines
指導教授:蔡鴻旭蔡鴻旭引用關係
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:109
中文關鍵詞:類神經網路支向量機人類視覺系統正好查覺差異離散小波轉換影像驗証資料隱藏浮水印
外文關鍵詞:Artificial Neural NetworksSupport Vector MachineHuman Visible SystemJust Noticeable DifferenceDiscrete Wavelet TransformImage AuthenticationData hidingWatermarking
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本論文提出三個浮水印技術來保護數位影像內容,分別為IWNN (Image watermarking based on neural networks)、DHIW (Decision-based hybrid image watermarking)及SVMLIW (SVM-based lossless image watermarking)。三者技術分別都使用到離散小波轉換(discrete wavelet transform)。在IWNN技術,利用HVS(human visible system)中JND(just noticeable difference)控制浮水印藏入的強度,並使用類神經網路(artificial neural network)中MLP(multi-layer perceptions)來記憶原始係數值及藏入浮水印後的係數值之間的關係。因此,藏匿完的浮水印影像具有高透明性並且抽取浮水印時不需使用到原始影像。由於在IWNN技術中處理影像紋理較複雜(texture)時,會影響類神經網路一般化(generalization)能力,故本論文提出DHIW技術,藉由兩個係數值關係來藏入浮水印及結合IWNN優點,以提昇整體浮水印抽出的準確率。
SVMLIW技術不修改數位原始影像下,可同時保護數位原始影像及植入擁有者(owner)簽章。主要找出小波係數中一些不變量的特徵值造出由原始影像所產生的浮水印。接著,以互斥或(XOR)方式來承載使用者的簽章以產生載體資訊(carried information)。再者,支向量機(support vector machine)用來記憶由原始影像所產生的浮水印及載體資訊兩者關係。最後,使用受訓練後的支向量機還原出使用者的簽章,以驗証原始影像。在模擬實驗結果下,本論文所提出IWNN技術、DHIW技術及SVMLIW技術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,這些技術能被有效運用至數位多媒體著作權保護及所有權鑑定。
The thesis presents three image-watermarking techniques, the IWNN (Image watermarking based on neural networks), the DHIW (Decision-based hybrid image watermarking) and the SVMLIW (SVM-based lossless image watermarking), to protect image copyrights. These techniques are developed in the wavelet domain. The IWNN technique employs the just noticeable difference (JND) profile, a characteristic of the human visual system (HVS) model, to control watermark-embedding strength, and utilizes an artificial neural network (ANN) to memorize the relationships between a set of the original wavelet blocks and its watermarked version. The IWNN technique makes watermarks further imperceptible, and doesn’t need the original image during watermark extraction. A drawback of the IWNN technique is that the ANN performs poor generalization ability if an image has highly complex textures, for example, Baboon image. This inspires us to propose the DHIW technique to overcome the drawback. A scheme hides a watermark bit in two coefficients of a wavelet block with complex textures. Therefore, a decision-based concept is applied to devise the DHIW technique which combines the IWNN technique with the scheme. As a result, the DHIW technique significantly improves the performance of the IWNN technique.
These two techniques mentioned above still degrade the visual quality of watermarked images due to some modifications to images during watermark embedding. A lossless image watermarking method, called the SVMLIW technique, is proposed to retain high visual quality of protected images. Although the SVMLIW technique doesn’t modify the original image, it can hide the owner’s information in an image by using the XOR operation and a support vector machine (SVM). A design concept is to generate an image-dependent watermark, a sequence of the invariant features of the wavelet blocks of an image. The SVMLIW technique applies the XOR operation to the image-dependent watermark and an owner’s signature to generate the corresponding carried information. Subsequently, a support vector machine is utilized to memorize the relationships between the image-dependent watermark and its corresponding carried information. During watermark extraction, the SVMLIW technique exploits the trained SVM to estimate the corresponding carried information without the original image, and then retrieves the owner’s signature by applying the XOR operation to the image-dependent watermark and the estimated carried information. Numerous computer simulations demonstrate that these three schemes are definitely robust to resist the common image-processing attacks, and that are significantly superior to other existing methods. Therefore, they can be effectively applied to protect multimedia contents for intellectual property and ownership identification.
誌謝 I
摘要 II
Abstract IV
目錄 VI
表目錄 IX
圖目錄 X
一、 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 論文架構 4
二、 文獻探討 5
2.1 離散小波轉換 5
2.1.1 一維離散小波轉換 5
2.1.2 二維離散小波轉換 6
2.1.3 Biorthogonal 9/7小波 8
2.2 人類視覺系統 8
2.3 影像表示 9
2.4 數位影像浮水印 11
2.4.1 數位影像浮水印背景 11
2.4.2 數位影像浮水印特性 12
2.4.3 數位影像浮水印技術 13
2.4.4 常見影像浮水印系統評估指標 16
2.5 類神經網路 17
2.5.1 單層感知機 17
2.5.2 倒傳遞類神經網路 18
2.5.3 常見的活化函數 22
2.6 支向量機 24
2.6.1 線性可分支向量機 24
2.6.2 線性不可分支向量機 26
2.6.3 非線性支向量機 28
三、 IWNN (Image watermarking based on neural networks)技術 31
3.1 架構簡介 31
3.1.1 問題描述 32
3.1.2 合理不可視範圍之JND門檻值推導 33
3.2 浮水印藏入 35
3.3 浮水印抽出 39
3.4 模擬實驗結果 41
3.4.1 透明性比較 42
3.4.2 強靭性比較 42
四、 DHIW (Decision-based hybrid image watermarking)技術 51
4.1 架構簡介 51
4.2 浮水印藏入 52
4.3 浮水印抽出 55
4.4 模擬實驗結果 57
4.4.1 區塊係數值挑選 57
4.4.2 透明性比較 58
4.4.2 強靭性測試 58
五、 SVMLIW (SVM-based lossless image watermarking)技術 71
5.1 架構簡介 71
5.2 浮水印產生及記憶使用者簽章 73
5.3 抽取使用者簽章 76
5.4 模擬實驗結果 77
5.4.1 挑選適合的影像特徵 77
5.4.2 記憶能力比較 89
5.4.3 選擇sliding window大小 90
5.4.4 強靭性測試 91
六、 結論及未來研究方向 99
6.1 結論 99
6.2 未來研究方向 100
參考文獻 101
附錄一 105
附錄二 109
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