隨著資訊科技之演進，數位產品的普及，使得數位影像的取得更為容易，其拍攝內容經常被廣泛應用在新聞報導、犯罪調查、科學研究等，因此數位影像之安全性，逐漸變成一個相當受到重視的議題。在本論文提出一個影像竄改偵測系統，對於複製-黏貼(copy-paste)竄改影像中複製區域經過旋轉、縮放處理後再黏貼之區域能有效的進行檢測，本論文所提出之方法是，首先將影像做區塊重疊分割，接著對分割的區塊進行極坐標轉換，之後沿著極座標的 軸之像素做累加運算，會得到一維描述子，再將此一維描述子使用Haar小波函數將一維描述子進行三階的一維離散小波轉換，取出其低頻部份的前面3個係數作為特徵向量，此特徵向量稱之為一維小波描述子，它具有旋轉、縮放、平移之不變性，並利用區塊的色彩和亮度等資訊作為區塊的特徵值，其維度為4，最後可得到一個維度為7維的特徵向量來表示一個區塊，如此可縮減區塊間的特徵向量排序及相似度匹配計算時間，進而提升系統整體效能。本文提出方法與相關參考文獻[19]做比較測試，測試影像資料採用Caltech-256 dataset資料庫與部分影像從網路上取得，從實驗結果中顯示本文所提出之方法在紋理複雜與平滑之竄改影像檢測效果優於相關參考文獻[19]。

With the evolution of information technology, the source of digital images is easily obtained from such as digital cameras, digital video cameras or surveillance system. These digital images are widely used in news reports, criminal investigations, and scientific research. How to ensure that the digital images are not tampered? In the recent years, the subject called digital image forensics security has gradually become a considerable issue. In this thesis, a passive detection method for detection copy-paste tampering of the digital image was proposed. The tampered portion can be detected even under the operations of rotation or/and scaling. Firstly, the image is divided into overlapped blocks of pixels. To achieve rotation invariant features, the overlapping blocks of pixels are mapped to log-polar coordinates, and then calculated the sum of pixel values along the angle theta axis, to produce a one-dimensional descriptor. Then, the Haar wavelet function is applied to the obtained one-dimensional(1-D) descriptor to obtain its 3rd-level wavelet transform. The first 3 coefficients of the low band frequency called 1-D wavelet descriptor are kept as the features of the block. This descriptor is invariant for rotating, scaling, and translating of the block. The color and intensity of the block are also adopted as the features. The feature vector of a block is consisted of these 7 features. All of these feature vectors are lexicographically sorted for reducing the searching time while finding the similar regions. For testing the performance of the proposed method and comparing with the similar research [19], some of the test data was adopted from Caltech-256 dataset and some are collected from Internet. It is shown that the proposed method is performed better than [19] for both of complex and smooth images.

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