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Defending visual adversarial examples with smoothout regularization

Descriptive

TitleInfo
Title
Defending visual adversarial examples with smoothout regularization
Name (type = personal)
NamePart (type = family)
Li
NamePart (type = given)
Weitian
NamePart (type = date)
1996-
DisplayForm
Weitian Li
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Yuan
NamePart (type = given)
Bo
DisplayForm
Bo Yuan
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Wei
NamePart (type = given)
Sheng
DisplayForm
Sheng Wei
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Spasojevic
NamePart (type = given)
Predrag
DisplayForm
Predrag Spasojevic
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
In the past decades with unexpected and rapid development of computer vision, tremendous computer vision applications like face recognition, image recognition, object detection and so on. They present their powerful abilities to made life so convenient for humans. In the trend of computer vision, deep neural networks (DNNs) occupies a very essential role. Because relative applications are deploying in many critical fields such as autonomous car, authentication and so on. However, there exist many adversarial attacks that can result in huge model performance degradation. Deploying a robust and reliable DNN is becoming a crucial and necessary step for various applications. In this work, we introduce SmoothBlock, a novel regularization method to improve the model robustness against adversarial attacks. It can be directly utilized as a defense mechanism in inference phase to protect the pre-trained model. Besides, the proposed SmoothBlock can also be applied in both training and adversarial training to further improve the robustness against various adversarial attacks. Furthermore, we apply the proposed SmoothBlock with a self-ensemble method to improve the robustness of the system. We conduct extensive trials and detailed analysis on CIFAR-10 using Resnet20 model. Results show that the model robustness can be significantly improved by our method against FGSM, PGD and C&W L2 attacks under white-box scenarios.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = local)
Topic
Adversarial example
Subject (authority = LCSH)
Topic
Neural networks (Computer science) -- Security measures
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10218
PhysicalDescription
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InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (viii, 32 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-pst2-0s09
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Li
GivenName
Weitian
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-05 14:59:42
AssociatedEntity
Name
Weitian Li
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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ContentModel
ETD
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windows xp
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1.5
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-09-11T21:13:16
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2019-09-11T19:26:53
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pdfTeX-1.40.18
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