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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3RNLR4H
Repositorysid.inpe.br/sibgrapi/2018/08.31.01.55
Last Update2018:08.31.01.55.55 administrator
Metadatasid.inpe.br/sibgrapi/2018/08.31.01.55.55
Metadata Last Update2020:02.19.03.10.44 administrator
Citation KeyMacedoNetoCostSant:2018:BeMeCh
TitleA Benchmark Methodology for Child Pornography Detection
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size397 KiB
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Author1 Macedo Neto, João José de
2 Costa, Filipe de Oliveira
3 Santos, Jefersson Alex dos
Affiliation1 UFMG
2 CPQD
3 UFMG
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressjoaomacedo@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-08-31 01:55:55 :: joaomacedo@gmail.com -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
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Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Transferable1
Content TypeExternal Contribution
Tertiary TypeFull Paper
Keywordschild pornography detection, benchmark dataset, deep learning, computer forensics.
AbstractThe 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.
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Languageen
Target Filesibgrapi_2018_paper_64.pdf
User Groupjoaomacedo@gmail.com
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Next Higher Units8JMKD3MGPAW/3RPADUS
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