1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPAW/3PF33M8 |
Repository | sid.inpe.br/sibgrapi/2017/08.16.19.40 |
Last Update | 2017:08.16.19.40.46 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2017/08.16.19.40.46 |
Metadata Last Update | 2022:06.14.00.08.42 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2017.16 |
Citation Key | RezendeRuppCarv:2017:DeCoGe |
Title | Detecting Computer Generated Images with Deep Convolutional Neural Networks |
Format | On-line |
Year | 2017 |
Access Date | 2024, Oct. 15 |
Number of Files | 1 |
Size | 964 KiB |
|
2. Context | |
Author | 1 Rezende, Edmar R. S. de 2 Ruppert, Guilherme C. S. 3 Carvalho, Tiago |
Affiliation | 1 CTI Renato Archer, Campinas-SP, Brazil 2 CTI Renato Archer, Campinas-SP, Brazil 3 Federal Institute of São Paulo (IFSP), Campinas-SP, Brazil |
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 | tiagojc@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ, Brazil |
Date | 17-20 Oct. 2017 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full 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 Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | Deep Learning Convolutional Neural Network Computer Generated Image Detection |
Abstract | Computer 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 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Detecting Computer Generated... |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Detecting Computer Generated... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
|
4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPAW/3PF33M8 |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PF33M8 |
Language | en |
Target File | sibgrapi-2017-detecting.pdf |
User Group | tiagojc@gmail.com |
Visibility | shown |
Update Permission | not transferred |
|
5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PKCC58 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2017/09.12.13.04 33 sid.inpe.br/sibgrapi/2022/06.10.21.49 5 sid.inpe.br/banon/2001/03.30.15.38.24 1 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
|
6. Notes | |
Empty Fields | archivingpolicy 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 |
|