本篇論文提出一種用於建構強健型數位影像浮水印之類神經網路特徵區域選取方法。此方法是從特徵偵測器所獲得之特徵區域中選出適當用於嵌入浮水印，以達到對任意的非惡意攻擊能具有最大的強健程度之目標。而此選擇方法共有二個階段包含使用類神經網路分類和選擇適當的特徵區域。類神經網路分類器是用來評估每一個特徵區域對於非惡意攻擊時所具有的抵抗能力。在第二階段會依據上述所得到的結果，在顧及影像品質下而選擇出能達到最大抵抗攻擊的特徵區域，而此部分則是屬於最佳化問題，在經由數學模型的轉換後形成一個多維度背包問題(MDKP)，因此就能套用現有的基因演算法解決此問題。而所選擇的特徵區域則是參考了特徵區域裡每一個像素的局部方向性和視覺感知調整權重值用來嵌入浮水印。經由類似的步驟就能夠擷取出浮水印而且不需再使用原始影像。而驗證方法則是選用了StirMark 3.1當作攻擊基準方式用來測試在嵌入浮水印的影像上。在本論文裡的實驗結果則顯現出所選擇的特徵區域的確可以提升對於任意的非惡意攻擊的強健程度。而更進一步的是此特徵區域浮水印演算法在強健程度上則是優於其他的方法。

In this thesis, a neural network-based determination of feature region is proposed for robust digital image watermarking algorithm. This method targets to select an adequate set of feature regions with maximum robustness to various non-malicious attacks from those obtained by any feature detector. It consists of two stages including the Genetic Algorithm-based Neural Network (GA-based NN) [HUA08] classification stage and the feature set selection stage. The neural network classifier is used to determine the resistance of each feature region against non-malicious attacks. According to the result of attack resistance, the feature regions are selected to achieve maximal resistance under an image visual quality in the second stage. This work is formulated as a multidimensional knapsack problem (MDKP) and solved by a heuristic approach based on genetic algorithm. The watermark is embedded into these selected regions according to the local orientation and perceptual weight of each pixel. Similarly, the proposed watermarking algorithm extracts the watermarks independently and blindly according to the above procedure. In order to verify the proposed method, the attacks adopted by the benchmark, StirMark 3.1, are applied to the watermarked images. The experimental results in this thesis reveal that the watermarked regions selected are able to increase the resistance to non-malicious attacks. Besides, the watermarking algorithm based on the proposed feature region method is confirmed to have better performance than the other methods compared.

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