Toward Intelligent Detection Modelling for Adversarial Samples in Convolutional Neural Networks

Zhuobiao Qiao, Mianxiong Dong, Kaoru Ota, Jun Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Deep Neural Networks (DNNs) are hierarchical nonlinear architectures that have been widely used in artificial intelligence applications. However, these models are vulnerable to adversarial perturbations which add changes slightly and are crafted explicitly to fool the model. Such attacks will cause the neural network to completely change its classification of data. Although various defense strategies have been proposed, existing defense methods have two limitations. First, the discovery success rate is not very high. Second, existing methods depend on the output of a particular layer in a specific learning structure. In this paper, we propose a powerful method for adversarial samples using Large Margin Cosine Estimate(LMCE). By iteratively calculating the large-margin cosine uncertainty estimates between the model predictions, the results can be regarded as a novel measurement of model uncertainty estimation and is available to detect adversarial samples by training using a simple machine learning algorithm. Comparing it with the way in which adversar- ial samples are generated, it is confirmed that this measurement can better distinguish hostile disturbances. We modeled deep neural network attacks and established defense mechanisms against various types of adversarial attacks. Classifier gets better performance than the baseline model. The approach is validated on a series of standard datasets including MNIST and CIFAR -10, outperforming previous ensemble method with strong statistical significance. Experiments indicate that our approach generalizes better across different architectures and attacks.

Original languageEnglish
Title of host publication2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538661512
DOIs
Publication statusPublished - 2018 Oct 29
Externally publishedYes
Event23rd IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2018 - Barcelona, Spain
Duration: 2018 Sep 172018 Sep 19

Publication series

NameIEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
Volume2018-September
ISSN (Electronic)2378-4873

Conference

Conference23rd IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2018
Country/TerritorySpain
CityBarcelona
Period18/9/1718/9/19

Keywords

  • Adversarial samples
  • CNN attacks and detec- tion
  • Large Margin Cosine Estimate

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design

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