指紋追蹤(fingerprinting)是數位浮水印中具有潛力的一種應用，主要被期望用來阻止非法的複製以及保護文件擁有者的智慧財產權。藉著嵌入代表接收者個人的浮水印，我們能夠根據那些從不合法的複製中擷取出來的隱藏訊號來追蹤散佈的來源。在這篇論文中，我們提出了兩個視訊的指紋追蹤的方法。考慮到視訊畫面可能會受到幾何上的修改，兩個方法皆採用了SIFT來解決這類的問題。更精確的說，在以特徵點為基礎的指紋追蹤方法中，我們根據尺度空間所找到的特徵點尺度以及方向來產生具有不變性的區域。而浮水印也將被嵌入至視訊編碼規格如MPEG2或MPEG4中的DCT係數或量化指標。由於在指紋追蹤的應用中並未強制地要求需要盲檢測，因此在我們第一個方法中，利用原始的畫面來將受到攻擊的畫面回復成原始的形狀才去偵測浮水印。而第二個方法則是不需要原始畫面來簡化浮水印的偵測。實驗結果證明所提出方法的可行性。Fingerprinting is one of the potential applications of digital watermarking, which is expected to be helpful in discouraging illegal copying and protecting the intellectual property rights of content owners. By embedding the watermark representing the individual ngerprint of the intended receiver in the content, we may trace down the source of distribution according to the extracted hidden signal of an illegal copy. In this research, we propose two video ngerprinting schemes for digital videos. Considering that the video frames may be geometrically modi ed, both of the schemes make use of Scale Invariant Feature Transform (SIFT) to deal with such attacks. To be more specific, our feature-based ngerprinting schemes employ the invariant regions of each specific frame based on the orientation and the scale of the scale-space feature points. The watermark will be embedded into DCT coefficients or quantization indices, which will appear in the coding structure of such video codec as MPEG2 or MPEG4. Our first scheme requires the original frames in the watermark detector for recovering the attacked frames into the original shape before the watermark detection as this application may not strictly require the blind watermark detection. The second scheme doesnot require the original frames to simplify the watermark detection. The experimental results will demonstrate the feasibility of the proposed methods.
