近年來電腦圖像技術力不斷在進步，若遭有心人士利用在竄改身分、偽造醜聞照片之類不法應用，後果將不堪設想，這使得辨識影像真偽成為現代科技的一門重大課題。在本論文中，藉由一種由去馬賽克演算法所造成的週期性特徵，提出一個辨識真實影像PIM和電腦圖像PRCG的理論，這種週期性現象在傅立葉空間特徵中測量為一種特徵，並結合一簡要的分類器去分辨真實影像和電腦影像。基於此理論的演算法組成如下:色彩空間轉換，高通濾波特徵萃取，和分類。實驗結果顯示，提出的理論不但可以偵測真實影像，還能判斷所使用的相機廠牌。與現存的方式比較，提出的理論擁有較高的辨認率，結果也展現出此理論的性能優於現存的方法。

In recent years, computer graphics technology is constantly in progress, if some people employ the technology in crime, such as falsi cation of identity, or forgeries of scandal photos, the consequences would be disastrous. Thus it makes Photorealistic computer graphics (PRCG) detection to become a major issue of modern technology. In this thesis, a scheme to distinguish photographic images (PIM) from PRCG images by using a periodic phenomenon of variance of pixel values resulting from demosaicing process is proposed. The periodic phenomenon is measured as a feature in Fourier domain feature and combined with a simple classi er to distinguish PIM images from PRCG images. Based on the phenomenon, the proposed scheme is composed of color space transformation, high-pass ltering feature extraction, and classi cation. Experimental results show that proposed scheme can not only detect PIM images but also determine what brand of a camera is used. Compared with existing methods, the precision and recall rates of proposed scheme are higher. The result shows that the performance of the proposed scheme is better than existing methods.

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