@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 = "29 Oct.-1 Nov. 2018",
doi = "10.1109/SIBGRAPI.2018.00065",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00065",
language = "en",
ibi = "8JMKD3MGPAW/3RNLR4H",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3RNLR4H",
targetfile = "sibgrapi_2018_paper_64.pdf",
urlaccessdate = "2024, May 02"
}