Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PJFC5L |
Repository | sid.inpe.br/sibgrapi/2017/09.07.01.51 |
Last Update | 2017:09.07.01.51.40 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/09.07.01.51.40 |
Metadata Last Update | 2021:02.23.03.53.29 administrator |
Citation Key | PerezTestRoch:2017:ViPoDe |
Title | Video pornography detection through deep learning techniques and motion information  |
Format | On-line |
Year | 2017 |
Access Date | 2021, Mar. 02 |
Number of Files | 1 |
Size | 1168 KiB |
Context area | |
Author | 1 Perez, Mauricio Lisboa 2 Testoni, Vanessa 3 Rocha, Anderson |
Affiliation | 1 EEE - NTU 2 Samsung Research Institute Brazil 3 IC - UNICAMP |
Editor | Torchelsen, 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 Address | mauriciolp84@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Date | Oct. 17-20, 2017 |
Book Title | Proceedings |
Publisher | Sociedade Brasileira de Computação |
Publisher City | Porto Alegre |
Tertiary Type | Master's or Doctoral Work |
History | 2017-09-07 01:51:40 :: mauriciolp84@gmail.com -> administrator :: 2021-02-23 03:53:29 :: administrator -> :: 2017 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
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. |
Arrangement | |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PJFC5L |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PJFC5L |
Language | en |
Target File | wtd-sibgrapi.pdf |
User Group | mauriciolp84@gmail.com |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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