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@InProceedings{PerezTestRoch:2017:ViPoDe,
               author = "Perez, Mauricio Lisboa and Testoni, Vanessa and Rocha, Anderson",
          affiliation = "{EEE - NTU} and {Samsung Research Institute Brazil} and {IC - 
                         UNICAMP}",
                title = "Video pornography detection through deep learning techniques and 
                         motion information",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Pornography classification, Deep learning and motion information, 
                         Optical flow, MPEG motion vectors, Sensitive video 
                         classification.",
             abstract = "Recent literature has explored automated pornographic detection - 
                         a bold move to replace humans in the tedious task of moderating 
                         online content. Unfortunately, on scenes with high skin exposure, 
                         such as people sunbathing and wrestling, the state of the art can 
                         have many false alarms. This paper is based on the premise that 
                         incorporating motion information in the models can alleviate the 
                         problem of mapping skin exposure to pornographic content, and 
                         advances the bar on automated pornography detection with the use 
                         of motion information and deep learning architectures. Deep 
                         Learning, especially in the form of Convolutional Neural Networks, 
                         have striking results on computer vision, but their potential for 
                         pornography detection is yet to be fully explored through the use 
                         of motion information. We propose novel ways for combining static 
                         (picture) and dynamic (motion) information using optical flow and 
                         MPEG motion vectors. We show that both methods provide equivalent 
                         accuracies, but that MPEG motion vectors allow a more efficient 
                         implementation. The best proposed method yields a classification 
                         accuracy of 97.9% - an error reduction of 64.4% when compared to 
                         the state of the art - on a dataset of 800 challenging test cases. 
                         Finally, we present and discuss results on a larger, and more 
                         challenging, dataset.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJFC5L",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PJFC5L",
           targetfile = "wtd-sibgrapi.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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