@InProceedings{RezendeRuppCarv:2017:DeCoGe,
               author = "Rezende, Edmar R. S. de and Ruppert, Guilherme C. S. and Carvalho, 
                         Tiago",
          affiliation = "CTI Renato Archer, Campinas-SP, Brazil and CTI Renato Archer, 
                         Campinas-SP, Brazil and Federal Institute of S{\~a}o Paulo 
                         (IFSP), Campinas-SP, Brazil",
                title = "Detecting Computer Generated Images with Deep Convolutional Neural 
                         Networks",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Deep Learning, Convolutional Neural Network, Computer Generated 
                         Image Detection.",
             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.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.16",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.16",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PF33M8",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PF33M8",
           targetfile = "sibgrapi-2017-detecting.pdf",
        urlaccessdate = "2024, Apr. 28"
}