臺灣博碩士論文加值系統

本研究提出在浮水印嵌入時，使用離散小波轉換（Discrete Wavelet Transformation, DWT）將圖片轉換成四個頻率高低不同的頻帶（Sub-band），隨機選取任一頻帶利用粒子群演算法（Particle Swarm Optimization, PSO）調整浮水印嵌入時或萃取時的三個重要參數：決定是否要嵌入浮水印的門檻值、嵌入到原始影像圖時的浮水印數值及萃取浮水印時的臨界值。最後再使用信號雜訊比（Peak Signal to Noise Ratio, PSNR）公式計算原始影像圖與被嵌入浮水印（或遭受攻擊）影像圖之間的差異；使用位元正確率（Bit Correct Ratio, BCR）公式計算原始浮水印與從被嵌入浮水印（或遭受攻擊）影像圖中萃取回來浮水印之間的差異，判斷此二值是否為本研究所認定之最好值，接著再加入攻擊驗證浮水印是否強韌，經由實驗模擬結果顯示，本研究所提出的方法可以找出最好參數值。

In this thesis, the embedded watermark using discrete wavelet transformation (DWT) is proposed to convert images into four various frequency sub-bands. Furthermore, Particle swarm optimization (PSO) is used to randomly select any sub-band to adjust three important parameters at the embedded or extraction watermark: the possible value for embedding watermark, the value of watermark embedded in the original image and the threshold value of watermark extraction. In the proposed approach, it is to determine the best values of peak signal to noise ratio (PSNR) and bit correct ratio (BCR) from the original image and the watermark embedded image. In addition, it is to verify that whether the watermark is robust or not by inserting some attacks. According to the simulation result, it is shown that the proposed approach can find the best values.

誌謝 Ⅰ
摘要 Ⅱ
ABSTRACT Ⅲ
目錄 IV
表錄 V
圖錄 XV
符號說明 XIX
一、緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 研究流程 2
二、文獻探討 4
2.1 Discrete Wavelet Transformation 4
2.2 Particle Swarm Optimization 7
三、研究方法 11
四、模擬結果 16
4.1 參數設定及符號說明 16
4.2 未加入攻擊 18
4.3 高斯雜訊攻擊 37
4.4 縮小攻擊 44
4.5 JPEG壓縮攻擊 57
五、結論與未來研究方向 95
參考文獻 96

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