植基於α-trimmed mean演算法與支向量機之強韌性無失真影像浮水印

Robust Lossless Watermarking Based on α-trimmed Mean Algorithm and Support Vector Machine

曾厚強(Hou-Chiang Tseng)

指導教授 ： 蔡鴻旭

國立虎尾科技大學/管理學院/資訊管理研究所/碩士(2008年)

https://doi.org/10.6827/NFU.2008.00012

摘要

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。

關鍵字

支向量機 ； 離散小波轉換 ； 無失真浮水印 ； 影像驗證

並列摘要

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.

並列關鍵字

Support Vector Machine ； Discrete Wavelet Transform ； Lossless Watermarking ； Image Authentication

參考文獻

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