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アイテム
Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images
http://hdl.handle.net/10228/00008493
http://hdl.handle.net/10228/00008493dfa9947c-516f-4941-ac11-cab3d0a9ed4b
| 名前 / ファイル | ライセンス | アクション |
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applsci-11-09556.pdf (763.5 kB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||||||
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| 公開日 | 2021-10-15 | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images | |||||||||||||
| 言語 | en | |||||||||||||
| その他のタイトル | ||||||||||||||
| その他のタイトル | Backdoor attacks to deep neural network-based system for COVID-19 detection from chest X-ray images | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 著者 |
Matsuo, Yuki
× Matsuo, Yuki× 竹本, 和広
WEKO
24877
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Open-source deep neural networks (DNNs) for medical imaging are significant in emergent situations, such as during the pandemic of the 2019 novel coronavirus disease (COVID-19), since they accelerate the development of high-performance DNN-based systems. However, adversarial attacks are not negligible during open-source development. Since DNNs are used as computer-aided systems for COVID-19 screening from radiography images, we investigated the vulnerability of the COVID-Net model, a representative open-source DNN for COVID-19 detection from chest X-ray images to backdoor attacks that modify DNN models and cause their misclassification when a specific trigger input is added. The results showed that backdoors for both non-targeted attacks, for which DNNs classify inputs into incorrect labels, and targeted attacks, for which DNNs classify inputs into a specific target class, could be established in the COVID-Net model using a small trigger and small fraction of training data. Moreover, the backdoors were effective for models fine-tuned from the backdoored COVID-Net models, although the performance of non-targeted attacks was limited. This indicated that backdoored models could be spread via fine-tuning (thereby becoming a significant security threat). The findings showed that emphasis is required on open-source development and practical applications of DNNs for COVID-19 detection. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : Applied Sciences 巻 11, 号 20, p. 9556-1-9556-10, 発行日 2021-10-14 |
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| 出版社 | ||||||||||||||
| 出版者 | MDPI | |||||||||||||
| DOI | ||||||||||||||
| 関連タイプ | isIdenticalTo | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.3390/app11209556 | |||||||||||||
| 日本十進分類法 | ||||||||||||||
| 主題Scheme | NDC | |||||||||||||
| 主題 | 548 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2076-3417 | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by/4.0/ | |||||||||||||
| 権利情報 | Copyright (c) 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | deep neural networks | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | medical imaging | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | backdoor attacks | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | security and privacy | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | COVID-19 | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| 査読の有無 | ||||||||||||||
| 値 | yes | |||||||||||||
| 研究者情報 | ||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000509_ja.html | |||||||||||||
| 論文ID（連携） | ||||||||||||||
| 値 | 10383286 | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 9546 | |||||||||||||
