OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object Detection
In the evolving landscape of deep neural network security, adversarial patch attacks present a serious challenge for object detection systems. We introduce <sc>OD-Shield</sc>, a novel defense approach that employs a convolutional autoencoder framework to detect and remove anomalous regio...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11021559/ |
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| _version_ | 1849332394340909056 |
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| author | Byeongchan Kim Heemin Kim Minjung Kang Hyunjee Nam Sunghwan Park Jaewoo Lee Il-Youp Kwak |
| author_facet | Byeongchan Kim Heemin Kim Minjung Kang Hyunjee Nam Sunghwan Park Jaewoo Lee Il-Youp Kwak |
| author_sort | Byeongchan Kim |
| collection | DOAJ |
| description | In the evolving landscape of deep neural network security, adversarial patch attacks present a serious challenge for object detection systems. We introduce <sc>OD-Shield</sc>, a novel defense approach that employs a convolutional autoencoder framework to detect and remove anomalous regions in patched images, subsequently reconstructing these regions to mitigate adversarial effects. The reconstructed images are then processed by the object detector, thereby restoring reliable performance under diverse attack scenarios. Distinctly model-agnostic, <sc>OD-Shield</sc> operates as a pre-processing step and can be applied to a wide range of tasks—including image classification and object detection—without compromising the fidelity of the original image. Experiments on benchmark datasets (COCO, Visdrone, and Argoverse) reveal that <sc>OD-Shield</sc> outperforms existing defenses by 13%–47% on COCO, highlighting its effectiveness in addressing critical security vulnerabilities. This work not only tackles the immediate threat of adversarial patches but also lays the foundation for future research into adaptive, resilient defense mechanisms that keep pace with evolving adversarial tactics. |
| format | Article |
| id | doaj-art-a9bf8343286e4310a10e2a6d8e90f774 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a9bf8343286e4310a10e2a6d8e90f7742025-08-20T03:46:12ZengIEEEIEEE Access2169-35362025-01-0113982429825210.1109/ACCESS.2025.357626411021559OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object DetectionByeongchan Kim0https://orcid.org/0009-0005-4280-4802Heemin Kim1https://orcid.org/0009-0007-1329-2240Minjung Kang2https://orcid.org/0009-0009-3857-1750Hyunjee Nam3https://orcid.org/0009-0004-5334-6416Sunghwan Park4https://orcid.org/0000-0002-0253-110XJaewoo Lee5https://orcid.org/0000-0001-5887-2184Il-Youp Kwak6https://orcid.org/0000-0002-7117-7669Department of Statistics and Data Science, Chung-Ang University, Seoul, South KoreaDepartment of Statistics and Data Science, Chung-Ang University, Seoul, South KoreaDepartment of Statistics and Data Science, Chung-Ang University, Seoul, South KoreaDepartment of Statistics and Data Science, Chung-Ang University, Seoul, South KoreaDepartment of Security Convergence Science, Chung-Ang University, Seoul, South KoreaDepartment of Industrial Security, Chung-Ang University, Seoul, South KoreaDepartment of Statistics and Data Science, Chung-Ang University, Seoul, South KoreaIn the evolving landscape of deep neural network security, adversarial patch attacks present a serious challenge for object detection systems. We introduce <sc>OD-Shield</sc>, a novel defense approach that employs a convolutional autoencoder framework to detect and remove anomalous regions in patched images, subsequently reconstructing these regions to mitigate adversarial effects. The reconstructed images are then processed by the object detector, thereby restoring reliable performance under diverse attack scenarios. Distinctly model-agnostic, <sc>OD-Shield</sc> operates as a pre-processing step and can be applied to a wide range of tasks—including image classification and object detection—without compromising the fidelity of the original image. Experiments on benchmark datasets (COCO, Visdrone, and Argoverse) reveal that <sc>OD-Shield</sc> outperforms existing defenses by 13%–47% on COCO, highlighting its effectiveness in addressing critical security vulnerabilities. This work not only tackles the immediate threat of adversarial patches but also lays the foundation for future research into adaptive, resilient defense mechanisms that keep pace with evolving adversarial tactics.https://ieeexplore.ieee.org/document/11021559/Adversarial patch attackadversarial patch defensepre-processing defenseobject detection |
| spellingShingle | Byeongchan Kim Heemin Kim Minjung Kang Hyunjee Nam Sunghwan Park Jaewoo Lee Il-Youp Kwak OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object Detection IEEE Access Adversarial patch attack adversarial patch defense pre-processing defense object detection |
| title | OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object Detection |
| title_full | OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object Detection |
| title_fullStr | OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object Detection |
| title_full_unstemmed | OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object Detection |
| title_short | OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object Detection |
| title_sort | od shield convolutional autoencoder based defense against adversarial patch attacks in object detection |
| topic | Adversarial patch attack adversarial patch defense pre-processing defense object detection |
| url | https://ieeexplore.ieee.org/document/11021559/ |
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