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@InProceedings{MacedoNetoCostSant:2018:BeMeCh,
               author = "Macedo Neto, Jo{\~a}o Jos{\'e} de and Costa, Filipe de Oliveira 
                         and Santos, Jefersson Alex dos",
          affiliation = "UFMG and CPQD and UFMG",
                title = "A Benchmark Methodology for Child Pornography Detection",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "child pornography detection, benchmark dataset, deep learning, 
                         computer forensics.",
             abstract = "The acquisition and distribution of child sexual content are some 
                         of the most important concerns for legislative systems and law 
                         enforcement agencies around the world. There is a great demand for 
                         automatic detection of child pornography, mainly due to the large 
                         amount of existent data and the facility someone can share this 
                         content over the internet. Although there are some proposed 
                         methods to automatically detect child pornography content in the 
                         literature, there is no available dataset to assess and compare 
                         the performance of these methods due to legal restrictions, 
                         considering that in many countries the distribution or possession 
                         of this material is a crime by Law. To mitigate this problem, we 
                         work with the Brazilian Federal Police to structure and organize a 
                         benchmark methodology for child pornography to make it possible 
                         the comparison of distinct categories of child pornography 
                         detectors. Therefore, we present in this paper the used 
                         methodology for the creation of a new annotated dataset of images 
                         of child pornography. We also propose a child pornography 
                         detection step-wise methodology based on automatic age estimation 
                         combined with a pornography detector, which is evaluated using the 
                         described benchmark dataset. The proposed approach achieved 
                         results (79.84% accuracy) that overcome two tools currently used 
                         by the Brazilian Federal Police.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "sibgrapi_2018_paper_64.pdf",
        urlaccessdate = "2020, Dec. 02"
}


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