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Experimental Exploration of the Power of Conditional GAN in Image Reconstruction-Based Adversarial Attack Defense Strategies
http://hdl.handle.net/10228/0002001221
http://hdl.handle.net/10228/0002001221c8480390-fd9e-48e9-89a4-755c0e4c240c
| 名前 / ファイル | ライセンス | アクション |
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10443718.pdf (2.7 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 | 2025-02-04 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Experimental Exploration of the Power of Conditional GAN in Image Reconstruction-Based Adversarial Attack Defense Strategies | |||||||||||
| 言語 | en | |||||||||||
| 著者 |
張, 海波
× 張, 海波
WEKO
35483
× Sakurai, Kouichi
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| 著作権関連情報 | ||||||||||||
| 権利情報 | Copyright (c) 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes on Data Engineering and Communications Technologies. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-57870-0_14. | |||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Adversarial attacks pose a significant threat to the reliability and security of deep learning models, particularly in image processing applications. Defending against these sophisticated manipulations requires innovative strategies, with Generative Adversarial Networks (GANs) emerging as a promising solution. This paper presents an experimental exploration of the power of conditional Generative Adversarial Networks (cGANs) in image reconstruction-based strategies for defending against adversarial attacks. Our study involves a comparative analysis of four distinct image reconstruction models: the traditional GAN-based Defense-GAN, the cGAN-based method exemplified by pix2pix, a hybrid approach combining pix2pix with perceptual loss, and a generator model centered around residual blocks. The results of our experiments demonstrate that cGAN models exhibit significantly enhanced efficacy in defending against adversarial attacks compared to other image reconstruction methods. This superiority is attributed to the inherent characteristics of cGANs, which we delve into in detail. The findings provide crucial insights for developing more robust defense strategies against adversarial attacks in diverse image processing and machine learning applications. | |||||||||||
| 言語 | en | |||||||||||
| 備考 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | The 38th International Conference on Advanced Information Networking and Applications (AINA-2024), April 17 - 19, 2024, Kitakyushu, Japan | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : Lecture Notes on Data Engineering and Communications Technologies 巻 47, p. 151-162, 発行日 2024-04-10 |
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| 出版社 | ||||||||||||
| 出版者 | Springer | |||||||||||
| 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 | AM | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||
| DOI | ||||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1007/978-3-031-57870-0_14 | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | PISSN | |||||||||||
| 収録物識別子 | 2367-4512 | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2367-4520 | |||||||||||
| 会議記述 | ||||||||||||
| 会議名 | The 38th International Conference on Advanced Information Networking and Applications (AINA-2024) | |||||||||||
| 言語 | en | |||||||||||
| 開始年 | 2024 | |||||||||||
| 開始月 | 04 | |||||||||||
| 開始日 | 17 | |||||||||||
| 終了年 | 2024 | |||||||||||
| 終了月 | 04 | |||||||||||
| 終了日 | 19 | |||||||||||
| 開催国 | JPN | |||||||||||
| 査読の有無 | ||||||||||||
| 値 | yes | |||||||||||
| 研究者情報 | ||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100001768_ja.html | |||||||||||
| 論文ID（連携） | ||||||||||||
| 値 | 10443718 | |||||||||||
| 連携ID | ||||||||||||
| 値 | 12564 | |||||||||||
