Effective defense against physically embedded backdoor attacks via clustering-based filtering

Effective defense against physically embedded backdoor attacks via clustering-based filtering

Abstract Backdoor attacks pose a severe threat to the integrity of machine learning models, especially in real-world image classification tasks. In such attacks, adversaries embed malicious behaviors triggered by specific patterns in the training data, causing models to misclassify whenever the trig...

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Bibliographic Details
Main Author: Mohammed Kutbi
Format: Article
Language:English
Published: Springer 2025-04-01
Series:Complex & Intelligent Systems
Subjects:
Machine learning security
Backdoor attacks
Data poisoning
Deep learning
Online Access:https://doi.org/10.1007/s40747-025-01876-y
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https://doi.org/10.1007/s40747-025-01876-y

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