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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PJFC5L
Repositorysid.inpe.br/sibgrapi/2017/09.07.01.51
Last Update2017:09.07.01.51.40 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.07.01.51.40
Metadata Last Update2022:05.18.22.18.25 (UTC) administrator
Citation KeyPerezTestRoch:2017:ViPoDe
TitleVideo pornography detection through deep learning techniques and motion information
FormatOn-line
Year2017
Access Date2025, July 14
Number of Files1
Size1168 KiB
2. Context
Author1 Perez, Mauricio Lisboa
2 Testoni, Vanessa
3 Rocha, Anderson
Affiliation1 EEE - NTU
2 Samsung Research Institute Brazil
3 IC - UNICAMP
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressmauriciolp84@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2017-09-07 01:51:40 :: mauriciolp84@gmail.com -> administrator ::
2022-05-18 22:18:25 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsPornography classification
Deep learning and motion information
Optical flow
MPEG motion vectors
Sensitive video classification
AbstractRecent 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.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > Video pornography detection...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PJFC5L
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJFC5L
Languageen
Target Filewtd-sibgrapi.pdf
User Groupmauriciolp84@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 44
sid.inpe.br/banon/2001/03.30.15.38.24 3
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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