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		<doi>10.1109/SIBGRAPI.2017.16</doi>
		<citationkey>RezendeRuppCarv:2017:DeCoGe</citationkey>
		<title>Detecting Computer Generated Images with Deep Convolutional Neural Networks</title>
		<format>On-line</format>
		<year>2017</year>
		<numberoffiles>1</numberoffiles>
		<size>964 KiB</size>
		<author>Rezende, Edmar R. S. de,</author>
		<author>Ruppert, Guilherme C. S.,</author>
		<author>Carvalho, Tiago,</author>
		<affiliation>CTI Renato Archer, Campinas-SP, Brazil</affiliation>
		<affiliation>CTI Renato Archer, Campinas-SP, Brazil</affiliation>
		<affiliation>Federal Institute of São Paulo (IFSP), Campinas-SP, Brazil</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>tiagojc@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ, Brazil</conferencelocation>
		<date>17-20 Oct. 2017</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>Deep Learning, Convolutional Neural Network, Computer Generated Image Detection.</keywords>
		<abstract>Computer graphics techniques for image generation are living an era where, day after day, the quality of produced content is impressing even the more skeptical viewer. Although it is a great advance for industries like games and movies, it can become a real problem when the application of such techniques is applied for the production of fake images. In this paper we propose a new approach for computer generated images detection using a deep convolutional neural network model based on ResNet-50 and transfer learning concepts. Unlike the state-of-the- art approaches, the proposed method is able to classify images between computer generated or photo generated directly from the raw image data with no need for any pre-processing or hand-crafted feature extraction whatsoever. Experiments on a public dataset comprising 9700 images show an accuracy higher than 94%, which is comparable to the literature reported results, without the drawback of laborious and manual step of specialized features extraction and selection.</abstract>
		<language>en</language>
		<targetfile>sibgrapi-2017-detecting.pdf</targetfile>
		<usergroup>tiagojc@gmail.com</usergroup>
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