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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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-02006-4 |
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