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Mobile applications for skin cancer detection are vulnerable to physical camera-based adversarial attacks
http://hdl.handle.net/10228/0002001700
http://hdl.handle.net/10228/00020017008d290024-3483-47e2-95b4-02480de08b8b
| 名前 / ファイル | ライセンス | アクション |
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10451545.pdf (1.6 MB)
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| アイテムタイプ | 共通アイテムタイプ(1) | |||||||||||
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| 公開日 | 2025-05-27 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Mobile applications for skin cancer detection are vulnerable to physical camera-based adversarial attacks | |||||||||||
| 言語 | en | |||||||||||
| 著者 |
Oda, Junsei
× Oda, Junsei
× 竹本, 和広
WEKO
24877
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| 著作権関連情報 | ||||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||
| 権利情報 | Copyright (c) The Author(s) 2025 This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
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| 言語 | en | |||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Skin cancer is one of the most prevalent malignant tumors, and early detection is crucial for patient prognosis, leading to the development of mobile applications as screening tools. Recent advances in deep neural networks (DNNs) have accelerated the deployment of DNN-based applications for automated skin cancer detection. While DNNs have demonstrated remarkable capabilities, they are known to be vulnerable to adversarial attacks, where carefully crafted perturbations can manipulate model predictions. The vulnerability of deployed medical mobile applications to such attacks remains largely unexplored under real-world conditions. Here, we investigate the susceptibility of three DNN-based medical mobile applications to physical adversarial attacks using transparent camera stickers under black-box conditions where internal model architectures are inaccessible. Through digital experiments with various DNN architectures trained on a publicly available skin lesion dataset, we first demonstrate that camera-based adversarial patterns can achieve high transferability across different models. Using these findings, we implement physical attacks by attaching optimized transparent stickers to mobile device cameras. Our results show that these attacks successfully manipulate application predictions, particularly for melanoma images, with attack success rates reaching 50–80% across all applications while maintaining visual imperceptibility. Notably, melanoma images showed consistently higher vulnerability compared to nevus images across all tested applications. To the best of our knowledge, this is the first demonstration of real-world adversarial vulnerabilities in deployed medical mobile applications, revealing significant security concerns where prediction manipulation could affect diagnostic processes. Our study demonstrates the importance of security evaluation in deploying such applications in clinical settings. | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : Scientific Reports 巻 15, p. 18119, 発行日 2025-05-24 |
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| 出版社 | ||||||||||||
| 出版者 | Nature Publishing Group | |||||||||||
| 言語 | en | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Deep neural networks | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Medical imaging | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Adversarial attacks | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Security and privacy | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 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 | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| DOI | ||||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1038/s41598-025-03546-y | |||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2045-2322 | |||||||||||
| 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 | 10451545 | |||||||||||
| 連携ID | ||||||||||||
| 値 | 14499 | |||||||||||
