%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/08.16.19.40
%2 sid.inpe.br/sibgrapi/2017/08.16.19.40.46
%@doi 10.1109/SIBGRAPI.2017.16
%T Detecting Computer Generated Images with Deep Convolutional Neural Networks
%D 2017
%A Rezende, Edmar R. S. de,
%A Ruppert, Guilherme C. S.,
%A Carvalho, Tiago,
%@affiliation CTI Renato Archer, Campinas-SP, Brazil
%@affiliation CTI Renato Archer, Campinas-SP, Brazil
%@affiliation Federal Institute of São Paulo (IFSP), Campinas-SP, Brazil
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ, Brazil
%8 17-20 Oct. 2017
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Deep Learning, Convolutional Neural Network, Computer Generated Image Detection.
%X 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.
%@language en
%3 sibgrapi-2017-detecting.pdf