BDEKD: mitigating backdoor attacks in NLP models via ensemble knowledge distillation

BDEKD: mitigating backdoor attacks in NLP models via ensemble knowledge distillation

Abstract Backdoor attacks present significant risks to the security of deep neural networks (DNNs) in NLP domain, as the attackers can covertly manipulate the model’s output behavior either by poisoning the training data or tampering model’s training process. This paper introduces a novel backdoor d...

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Bibliographic Details
Main Authors: Zijie Zhang, Xinyuan Miao, Chenyu Zhou, Chenming Shang, Xi Chen, Xianglong Kong, Wei Huang, Yi Cao
Format: Article
Language:English
Published: Springer 2025-07-01
Series:Complex & Intelligent Systems
Subjects:
Backdoor defense
Natural language processing
Knowledge distillation
Ensemble learning
Online Access:https://doi.org/10.1007/s40747-025-02006-4
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https://doi.org/10.1007/s40747-025-02006-4

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