Abstract
Currently, some photorealistic computer graphics are very similar to photographic images. Photorealistic computer generated graphics can be forged as photographic images, causing serious s ecurity problems. The aim of this work is to use a deep neural network to detect photographic images ( P I) versus computer generated graphics (CG). In existing approaches, image feature classification is computationally intensive and fails to achieve real - t ime analysis. This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks (DCNNs). Compared with some existing methods, the proposed method achieves real - time forensic tasks by deepening the net work structure. Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.
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