Close
Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PK8JAB
Repositorysid.inpe.br/sibgrapi/2017/09.11.16.27
Last Update2017:09.11.16.27.29 (UTC) tabacof@gmail.com
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.11.16.27.29
Metadata Last Update2022:05.18.22.18.26 (UTC) administrator
Citation KeyTabacofVall:2017:ExAdIm
TitleExploring Adversarial Images in Deep Neural Networks
FormatOn-line
Year2017
Access Date2024, May 02
Number of Files1
Size2484 KiB
2. Context
Author1 Tabacof, Pedro
2 Valle, Eduardo
Affiliation1 University of Campinas
2 University of Campinas
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 Addresstabacof@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)2017-09-11 16:27:29 :: tabacof@gmail.com -> administrator ::
2022-05-18 22:18:26 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsdeep learning
neural networks
adversarial images
AbstractAdversarial examples have raised questions regarding the robustness and security of deep neural networks. In this work we formalize the problem of adversarial images given a pre-trained classifier, showing that even in the linear case the resulting optimization problem is nonconvex. We generate adversarial images using deep classifiers on the ImageNet dataset. We probe the pixel space of adversarial images using noise of varying intensity and distribution. We bring novel visualizations that showcase the phenomenon and its high variability. We show that adversarial images appear in large regions in the pixel space, and that it is hard to leave those regions by adding noise to the images, even with high intensity.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > Exploring Adversarial Images...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 11/09/2017 13:27 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PK8JAB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PK8JAB
Languageen
Target Filesibgrapi (1).pdf
User Grouptabacof@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 9
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


Close