WEKO3
アイテム
Towards Autonomous Driving Model Resistant to Adversarial Attack
http://hdl.handle.net/10061/0002000489
http://hdl.handle.net/10061/00020004894d01c2f5-a606-4fa5-8e39-1f9dc0fb10bb
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-06-27 | |||||||||
| タイトル | ||||||||||
| タイトル | Towards Autonomous Driving Model Resistant to Adversarial Attack | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ | journal article | |||||||||
| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| 著者 |
Shibly, Kabid Hassan
× Shibly, Kabid Hassan
× Hossain, Md_Delwar× Inoue, Hiroyuki
× 妙中, 雄三× 門林, 雄基 |
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Connected and Autonomous Vehicles (CAVs) offer improved efficiency and convenience through innovative embedded devices. However, the development of these technologies has often neglected security measures, leading to vulnerabilities that can be exploited by hackers. Conceding that a CAV system is compromised, it can result in unsafe driving conditions and pose a threat to human safety. Prioritizing both security measures and functional enhancements on development of CAVs is essential to ensure their safety and reliability and enhance consumer trust in the technology. CAVs use artificial intelligence to control their driving behavior, which can be easily influenced by small changes in the model that can significantly impact and potentially mislead the system. To address this issue, this study proposed a defense mechanism that uses an autoencoder and a compressive memory module to store normal image features and prevent unexpected generalization on adversarial inputs. The proposed solution was studied against Hijacking, Vanishing, Fabrication, and Mislabeling attacks using FGSM and AdvGAN against the Nvidia Dave-2 driving model, and was found to be effective, with success rates of 93.8% and 91.2% in a Whitebox setup, and 74.1% and 64.4% in a Blackbox setup for FGSM and AdvGAN, respectively. That improves the results by 24.7% in Whitebox setup 21.5% in Blackbox setup. |
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| 書誌情報 |
en : Applied Artificial Intelligence 巻 37, 号 1, 発行日 2023-03-24 |
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| 出版者 | ||||||||||
| 出版者 | Taylor & Francis | |||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 1087-6545 | |||||||||
| 出版者版DOI | ||||||||||
| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1080/08839514.2023.2193461 | |||||||||
| 出版者版URI | ||||||||||
| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | URI | |||||||||
| 関連識別子 | https://www.tandfonline.com/doi/full/10.1080/08839514.2023.2193461 | |||||||||
| 権利 | ||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by-nc/4.0/ | |||||||||
| 権利情報 | $00A9 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in anymedium, provided the original work is properly cited. The terms on which this article has been published allow the posting of theAccepted Manuscript in a repository by the author(s) or with their consent. | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | NA | |||||||||
