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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PF33M8
Repositorysid.inpe.br/sibgrapi/2017/08.16.19.40
Last Update2017:08.16.19.40.46 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.16.19.40.46
Metadata Last Update2022:06.14.00.08.42 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.16
Citation KeyRezendeRuppCarv:2017:DeCoGe
TitleDetecting Computer Generated Images with Deep Convolutional Neural Networks
FormatOn-line
Year2017
Access Date2024, Apr. 28
Number of Files1
Size964 KiB
2. Context
Author1 Rezende, Edmar R. S. de
2 Ruppert, Guilherme C. S.
3 Carvalho, Tiago
Affiliation1 CTI Renato Archer, Campinas-SP, Brazil
2 CTI Renato Archer, Campinas-SP, Brazil
3 Federal Institute of São Paulo (IFSP), Campinas-SP, Brazil
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 Addresstiagojc@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-16 19:40:46 :: tiagojc@gmail.com -> administrator ::
2022-06-14 00:08:42 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsDeep Learning
Convolutional Neural Network
Computer Generated Image Detection
AbstractComputer graphics techniques for image generation are living an era where, day after day, the quality of produced content is impressing even the more skeptical viewer. Although it is a great advance for industries like games and movies, it can become a real problem when the application of such techniques is applied for the production of fake images. In this paper we propose a new approach for computer generated images detection using a deep convolutional neural network model based on ResNet-50 and transfer learning concepts. Unlike the state-of-the- art approaches, the proposed method is able to classify images between computer generated or photo generated directly from the raw image data with no need for any pre-processing or hand-crafted feature extraction whatsoever. Experiments on a public dataset comprising 9700 images show an accuracy higher than 94%, which is comparable to the literature reported results, without the drawback of laborious and manual step of specialized features extraction and selection.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Detecting Computer Generated...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Detecting Computer Generated...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PF33M8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PF33M8
Languageen
Target Filesibgrapi-2017-detecting.pdf
User Grouptiagojc@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 6
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 volume


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