OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object Detection

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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Bibliographic Details
Main Authors: Byeongchan Kim, Heemin Kim, Minjung Kang, Hyunjee Nam, Sunghwan Park, Jaewoo Lee, Il-Youp Kwak
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Adversarial patch attack
adversarial patch defense
pre-processing defense
object detection
Online Access:https://ieeexplore.ieee.org/document/11021559/
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https://ieeexplore.ieee.org/document/11021559/

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