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MIC: An Effective Defense Against Word-Level Textual Backdoor Attacks

  • 2024
  • OriginalPaper
  • Chapter
Published in:
Authors
Shufan Yang
Qianmu Li
Zhichao Lian
Pengchuan Wang
Jun Hou
Published in
Neural Information Processing
Publisher
Springer Nature Singapore

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Abstract

The chapter 'MIC: An Effective Defense Against Word-Level Textual Backdoor Attacks' explores the vulnerabilities of NLP models to backdoor attacks, particularly focusing on word-level attacks. It introduces the MIC defense method, which incorporates metric learning and contrastive learning to prevent backdoor implantation and improve model robustness. The method is evaluated on several datasets, demonstrating superior performance compared to existing defense techniques. The chapter highlights the importance of preemptive defense strategies in model training to mitigate the risks posed by stealthy backdoor attacks.
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next chapter Active Learning for Open-Set Annotation Using Contrastive Query Strategy
Title
MIC: An Effective Defense Against Word-Level Textual Backdoor Attacks
Authors
Shufan Yang
Qianmu Li
Zhichao Lian
Pengchuan Wang
Jun Hou
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Neural Information Processing

Print ISBN: 978-981-9980-75-8

Electronic ISBN: 978-981-9980-76-5

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-99-8076-5_1
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