Distinguishing Computer Graphics from Natural Images Using Convolution Neural Networks

Nicolas Rahmouni; Vincent Nozick; Junichi Yamagishi; Isao Echizen

Distinguishing Computer Graphics from Natural Images Using Convolution Neural Networks

Nicolas Rahmouni ¹ Vincent Nozick ² Junichi Yamagishi ³ Isao Echizen ³

2 JFLI - Japanese French Laboratory for Informatics

3 NII - National Institute of Informatics

Abstract : This paper presents a deep-learning method for distinguishing computer generated graphics from real photographic images. The proposed method uses a Convolutional Neural Network (CNN) with a custom pooling layer to optimize current best-performing algorithms feature extraction scheme. Local estimates of class probabilities are computed and aggregated to predict the label of the whole picture. We evaluate our work on recent photo-realistic computer graphics and show that it outperforms state of the art methods for both local and full image classification.

Document type :

Conference papers

Domain :

Computer Science [cs] / Computer Vision and Pattern Recognition [cs.CV]

Computer Science [cs] / Image Processing

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https://hal-upec-upem.archives-ouvertes.fr/hal-01664590
Contributor : Vincent Nozick <>
Submitted on : Friday, December 15, 2017 - 8:06:33 AM
Last modification on : Monday, March 4, 2019 - 2:22:27 PM

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Distinguishing Computer Graphics from Natural Images Using Convolution Neural Networks

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