本論文提出一個結合 DCT 與 SVD 的數位浮水印技術，並透過多目標基因演 算法來解決浮水印的最佳化問題，即在不可視性與強韌性之間取得一個平衡點。
使用多目標基因演算法的好處在於，最後會產生一組最適解，使用者可以從中挑 選自己所偏好的解，提供了一個類似客製化的選擇。在實驗中，也將本論文提出 的浮水印技術改以單目標基因演算法求取最佳解；結果證實，多目標基因演算法 比單目標不僅更穩定，而且最適解的適應值也比較好。為了證實本論文所提出的 方法，不僅可以兼顧強韌性，還可以兼顧到嵌入浮水印後的影像品質，我們與 Monemizadeh 和 Seyedin[37]提出一個結合 DWT 與 SVD 的浮水印技術進行比 較。實驗結果顯示，我們提出的方法在兼顧到嵌入浮水印後的影像品質的狀況 下，還可以抵抗大部分的影像攻擊，說明了我們的方法不僅具有很好的強韌性，
在不可視性上也不輸給他人。
針對本論文提出的方法，未來主要的研究重點有：
(a) 增加對某些特定攻擊的抵抗性：
從實驗結果可得知，本方法對於旋轉、銳利化、剪裁等攻擊，有著較弱的抵 抗性。針對這個問題，或許可以試著在求取最佳解的過程中，使用其他的影 像攻擊進行訓練；或是考慮加入其他轉換域的嵌入方法，例如：DWT；亦 或考慮將人類視覺系統(human visual systems)應用於本方法中，進而改善浮 水印的強韌性。
(b) 縮短整體的運算時間：
本方法在多目標最佳化的過程中，需要較多的演化代數，以便求得較穩定的 最佳解集合。由於演化代數的提高，加上本方法使用 DCT 轉換域的嵌入方 法，使得整體的運算時間大幅的增加；故如何以較少的演化代數，求得更穩 定的解答，也是我們未來研究的重點之一。針對改善運算時間的問題，或許
可以試著調整 NSGA-II 的參數(例如：交配與突變的機率、分布索引、…等)；
或是考慮其他多目標最佳化演算法，例如：SPEA2(strength pareto evolutionary algorithm 2)、PAES(the pareto archived evolution strategy)、…等，進而改善運 算時間過於冗長的問題。
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