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  1. シンポジウム
  2. シンポジウムシリーズ
  3. コンピュータセキュリティシンポジウム
  4. 2019

A Label-Based System for Detecting Adversarial Examples by Using Low Pass Filters

https://ipsj.ixsq.nii.ac.jp/records/201484
https://ipsj.ixsq.nii.ac.jp/records/201484
a1936fc3-6f2d-4953-8c72-3918df8ab20c
名前 / ファイル ライセンス アクション
IPSJCSS2019191.pdf IPSJCSS2019191.pdf (1.5 MB)
Copyright (c) 2019 by the Information Processing Society of Japan
オープンアクセス
Item type Symposium(1)
公開日 2019-10-14
タイトル
タイトル A Label-Based System for Detecting Adversarial Examples by Using Low Pass Filters
タイトル
言語 en
タイトル A Label-Based System for Detecting Adversarial Examples by Using Low Pass Filters
言語
言語 eng
キーワード
主題Scheme Other
主題 Deep Neural Networks,Adversarial Examples,Low pass filter
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_5794
資源タイプ conference paper
著者所属
情報セキユリテイ大学院大学/University of Danang
著者所属
情報セキユリテイ大学院大学
著者所属
情報セキユリテイ大学院大学
著者所属(英)
en
Institute of Information Security / University of Danang
著者所属(英)
en
Institute of Information Security
著者所属(英)
en
Institute of Information Security
著者名 ダンデユイ, タン

× ダンデユイ, タン

ダンデユイ, タン

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近藤, 大生

× 近藤, 大生

近藤, 大生

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松井, 俊浩

× 松井, 俊浩

松井, 俊浩

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著者名(英) Thang, Dang Duy

× Thang, Dang Duy

en Thang, Dang Duy

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Taisei, Kondo

× Taisei, Kondo

en Taisei, Kondo

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Toshihiro, Matsui

× Toshihiro, Matsui

en Toshihiro, Matsui

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論文抄録
内容記述タイプ Other
内容記述 Along with significant improvements in deep neural networks, image classification tasks are solved with extremely high accuracy rates. However, deep neural networks have been recently found vulnerable to well-designed input samples that called adversarial examples. Such an issue causes deep neural networks to misclassify adversarial examples that are imperceptible to humans. Distinguishing adversarial images and legitimate images are tough challenges. To address this problem, in this paper we propose a new automatic classification system for adversarial examples. Our proposed system can almost distinguish adversarial samples and legitimate images in an end-to-end manner without human intervention. We exploit the important role of low frequencies in adversarial samples and proposing the label-based method for detecting malicious samples based on our observation. We evaluate our method on a variety of standard benchmark datasets including MNIST and ImageNet. Our method reached out detection rates of more than 96% in many settings.
論文抄録(英)
内容記述タイプ Other
内容記述 Along with significant improvements in deep neural networks, image classification tasks are solved with extremely high accuracy rates. However, deep neural networks have been recently found vulnerable to well-designed input samples that called adversarial examples. Such an issue causes deep neural networks to misclassify adversarial examples that are imperceptible to humans. Distinguishing adversarial images and legitimate images are tough challenges. To address this problem, in this paper we propose a new automatic classification system for adversarial examples. Our proposed system can almost distinguish adversarial samples and legitimate images in an end-to-end manner without human intervention. We exploit the important role of low frequencies in adversarial samples and proposing the label-based method for detecting malicious samples based on our observation. We evaluate our method on a variety of standard benchmark datasets including MNIST and ImageNet. Our method reached out detection rates of more than 96% in many settings.
書誌レコードID
識別子タイプ NCID
関連識別子 ISSN 1882-0840
書誌情報 コンピュータセキュリティシンポジウム2019論文集

巻 2019, p. 1356-1363, 発行日 2019-10-14
出版者
言語 ja
出版者 情報処理学会
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