在數位化時代來臨的今天，許多的媒體都以數位化的形式記錄、呈現，而數位影像雖然容易取得，卻也容易經由人為合成修改，因此該如何確認其攝影內容的完整性與真實性是一件越來越重要的議題。本論文提出一個被動式數位影像鑑識系統，此系統不需要依賴任何預先嵌入的資訊來驗證數位影像之真實性，亦不需要由使用者手動選取檢測區域。本研究分別利用二階導數之頻譜特徵與馬可夫特徵對未壓縮與壓縮過之影像進行來源相機機型辨識，並藉由所得到的相機機型取出其相對應之參考樣板雜訊與目前測試影像之樣板雜訊做比對，最後使用樣板雜訊之區塊相關性來定位出影像中被竄改的區域。由實驗結果可以顯示出本系統對於一般拍攝環境下之拼接竄改影像具有不錯的檢測效果。

In this digital era, all kinds of media data are recorded and presented digitally. The digital devices are popular. However, even though the digital photos is easy to obtain, it is also easily forged by digital tool. It brings up the needs for new digital data applications such like digital forensics. In this research, a passive spliced forgery detection system for digital image is proposed. This system does not depend on any pre-embedded information to verify the authenticity of the digital image, and the user doesn’t need select the detection area manually. This study used the spectral of the second derivative characteristics and Markov features on the uncompressed and compressed images of the camera models recognition. Then the sensor noise pattern is pre-evaluated by averaging the noise of this camera images and used to determine the tampered region for test image. The experimental results show that the detection rates of the camera models recognition are above 90%, which results for uncompressed data are even greater than 92%. Moreover, the system can locate the tampering region precisely.

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