A Textual Backdoor Defense Method Based on Deep Feature Classification.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Natural Language Processing

Background:

• Deep neural networks (DNNs) in natural language processing (NLP) are susceptible to sophisticated backdoor attacks.
• Current defense strategies for NLP models exhibit limitations in effectiveness and applicability across diverse scenarios.
• Backdoor attacks pose a significant threat to the integrity and reliability of NLP systems.

Purpose of the Study:

• To develop and evaluate a novel textual backdoor defense method for NLP models.
• To address the limitations of existing defense mechanisms by exploiting deep feature characteristics.
• To provide a robust defense applicable in both offline and online settings.

Main Methods:

• Proposed a backdoor defense method centered on deep feature classification for NLP models.
• Implemented deep feature extraction and subsequent classifier construction to differentiate benign and poisoned data.
• Validated the defense strategy across two distinct datasets and two different model architectures.

Main Results:

• The proposed defense method demonstrated significant effectiveness in identifying and mitigating backdoor attacks.
• Experimental results confirmed the superiority of the developed approach over baseline defense techniques.
• The method successfully distinguished between deep features of poisoned and benign data.

Conclusions:

• The deep feature classification approach offers a promising and effective defense against backdoor attacks in NLP models.
• The developed method provides a viable solution for enhancing the security of DNN-based NLP systems.
• The defense strategy is robust and applicable in various operational contexts, including offline and online scenarios.