隨著高畫質數位攝影相機普及與快速發展的網際網路，人們正生活在充滿數位影像及影片的世代。我們每天都會在智慧型手機、網站、社群媒體上看到各式影片，然而越來越多強大的數位影像/影片編輯軟體推陳出新，例如：Adobe Photoshop, Video Studio, Power Director ……等等，這些軟體使竄改影片更加容易且不會留下明顯的痕跡，這讓我們無法藉由人眼去辨識影片的真偽，因此竄改影像/影片偵測已成為確保數位數據真實性的關鍵要求。
在本篇論文中提出了兩種影片竄改辨識與定位技術，分別是基於視頻內部影像關西係數的video-based影片竄改偵測與基於分類回歸樹的image-based影片竄改偵測。video-based影片竄改偵測可判別影片是否遭到Temporal-domain的竄改並定位出相互複製的影片片段。image-based影片竄改偵測則可以分辨影片是否遭受到Spatial-domain的竄改並且定位出影格中的竄改區域。
實驗測試中，我們使用Surrey University Library for Forensic Analysis (SULFA)偽造影片序列的實驗資料庫來檢測本論文提出方法的性能，在SULFA偽造影片序列的實驗資料庫中包含10部原始影片與10部竄改影片，解析度為320×240畫面更新率為40FPS，檔案格式為AVI，其中竄改影片又分成兩種類型的竄改，分別是4部Temporal-domain 竄改影片以及6部Spatial-domain竄改影片。最後我們列出近期的論文方法的實驗結果與我們進行比較，結果顯示本論文所提出的兩種方法，在限制相對狹小的情況下，皆具有很高的分類準確度，並且可定位出影片中的竄改區域。

With the advent of high-quality digital video cameras and the rapid development of the internet, people are living in a full digital image/video generation. We may watch a lot of digital video on smart phone, website and community media every day. However, many and many powerful digital editing software was increasingly developed (e.g. Adobe Photoshop, Video Studio, Power Director…), tampering digital image /video is much easier and without leaving any traces. As a result the truth of image/video content can no longer be taken by human eyes. In this case, detection of tampered video has become a critical requirement to ensure the truth of digital video data, therefore, how to develop a video authentication system is a very important research.
In this thesis, we propose two tampered video detection and localization techniques. One is video-based tampered video detection and localization based on Inter-frame Correlation and the other is image-based tampered video detection and localization based on Classification and Regression Tree. The video-based tampered video detection and localization can detect temporal-domain forgery and locate the duplicate frames. Then the image-based tampered video detection and localization can detect spatial-domain forgery and locate the tampered region of the tampered frame.
In our experiments, we apply Surrey University Library for Forensic Analysis (SULFA) database of realistic forged sequences to evaluate our video authentication techniques. In SULFA database has 10 original videos and 10 tampered videos which are all 40FPS AVI format and the frame size is primarily 320×240. These tampered videos were divided into 4 temporal-domain forgery videos and 6 spatial-domain forgery videos. Finally, we list the recent method of paper to compared with our experimental results. The results show that the two methods presented in this paper are all with high classification accuracy in the case of relatively narrow limits, and also has the ability to locate the video tampering area.

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