Identifying Materials of Photographic Images and Photorealistic Computer Generated Graphics Based on Deep CNNs

Qi Cui; Suzanne McIntosh; Huiyu Sun

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Identifying Materials of Photographic Images and Photorealistic Computer Generated Graphics Based on Deep CNNs

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摘要

Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime 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 network structure.Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.

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《计算机、材料和连续体（英文）》 |2018年第5期|P.229-241|共13页
作者
Qi Cui; Suzanne McIntosh; Huiyu Sun;
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作者单位

School of Computer and Software Nanjing University of Information Science and Technology Ning Liu Road No.219 Nanjing 210044 ChinaJiangsu Engineering Centre of Network Monitoring Ning Liu Road No.219 Nanjing 210044 China;

Computer Science Department New York University New York NY 10012 USA;

Computer Science Department New York University New York NY 10012 USA;

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原文格式 PDF
正文语种 chi
中图分类计算技术、计算机技术;
关键词
Image identification; CNN; DNN; DCNNs; computer generated graphics;

机译：图像识别;CNN;DNN;DCNNS;计算机生成的图形;

Identifying Materials of Photographic Images and Photorealistic Computer Generated Graphics Based on Deep CNNs

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