對於修改影像大小到目標撥放設備的大小來說，以保護內容為基礎的影響重新定位演算法(Content-award image retargeting algorithm)是一種非常有用的技術。而在重新定位演算法中縫隙修改(Seam-carving)是其中一種既容易實作又能夠達到好成果的演算法。在這篇碩士論文中，我們提出了一項技術能夠在沒有原圖參考的情況底下區分出這張JPEG影像是否有經過縫隙修改(Seam-carving)這項科技的偽造。這項技術主要是以方格特徵區域矩陣(blocking effect characteristics matrix)為基礎。從細節上來說，對於原本沒有經過破壞的JPEG影像，方格特徵區域矩陣會呈現出對稱且完整的圖形。反過來說，當這個JPEG影像受到破壞或偽造時，方格特徵區域矩陣的規律將會被破壞。當我們重影像中計算出方格特徵區域矩陣之後，我們再由其中計算出22個特徵向量，並且將這些特徵向量經由支持向量機(Support Vector Machine)來做訓練來求得模組用來辨識經由縫隙修改的影像偽造，實驗數據中顯示我們所提出來的方法利用方格特徵區域矩陣中所擷取出來的特徵向量對於辨識縫隙修改的影像偽造技術的準確性高於現有的方法。 The Content-award image retargeting algorithm is used for modifying the image size into the suitable size in different device. “Seam carving” is a kind of content aware image retargeting algorithm. In this paper, based on the blocking artifact characteristics matrix (BACM), we propose a method to detect seam carving in natural images without knowledge of the original image. In detail, for the original JPEG images, the BACM exhibits regular symmetrical shapes for the images that are damaged, the regular symmetrical property of the BACM is destroyed. After found BACM from images, we define 22 features to detect the damage from BACM to train a support vector machine (SVM) classifier for recognizing whether an image is an original or it has been modified by seam-carving. We show that BACM is useful for detecting the damage by seam-carving in JPEG format images.
