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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11021559/ |
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