Identifying photorealistic computer graphics using convolutional neural networks

As computer graphics technology advances, it is becoming increasingly difficult to determine whether a given picture was taken by camera or via computer graphics. In this work, we propose a method to using simple CNN structures to identify photorealistic computer graphics (PRCG) using convolutional...

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Bibliographic Details
Main Authors: Yu, In-Jae, Kim, Do-Guk, Park, Jin-Seok, Hou, Jong-Uk, Choi, Sunghee, Lee, Heung-Kyu
Format: Conference Proceeding
Language:English
Subjects:
Computer graphics
Convolutional neural networks
Digital Forensics
Feature extraction
Graphics processing units
Image Source Identification
Photo-Realistic Computer Graphics
Robustness
Training
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Summary:As computer graphics technology advances, it is becoming increasingly difficult to determine whether a given picture was taken by camera or via computer graphics. In this work, we propose a method to using simple CNN structures to identify photorealistic computer graphics (PRCG) using convolutional neural networks (CNN). This network trained to identify the source of image patches. We showed the network without pooling layer showed 98.2% accuracy, which is 2.1% higher than the result of using conventional object-recognition network. Testing random patches from image, the accuracy of identifying image reached 98.5%. Furthermore, it is possible to detect the photograph-PRCG synthesized regions from the image.
ISSN:2381-8549
DOI:10.1109/ICIP.2017.8297052