Attention-Enhancing Backdoor Attacks Against BERT-based Models
This work addresses vulnerabilities in BERT-based models for NLP applications, representing an incremental advancement in backdoor attack strategies.
The paper tackled the problem of backdoor attacks in NLP models by proposing a Trojan Attention Loss (TAL) that manipulates attention patterns to enhance attack efficacy, achieving improved attack success and poisoning rates across various models and tasks.
Recent studies have revealed that \textit{Backdoor Attacks} can threaten the safety of natural language processing (NLP) models. Investigating the strategies of backdoor attacks will help to understand the model's vulnerability. Most existing textual backdoor attacks focus on generating stealthy triggers or modifying model weights. In this paper, we directly target the interior structure of neural networks and the backdoor mechanism. We propose a novel Trojan Attention Loss (TAL), which enhances the Trojan behavior by directly manipulating the attention patterns. Our loss can be applied to different attacking methods to boost their attack efficacy in terms of attack successful rates and poisoning rates. It applies to not only traditional dirty-label attacks, but also the more challenging clean-label attacks. We validate our method on different backbone models (BERT, RoBERTa, and DistilBERT) and various tasks (Sentiment Analysis, Toxic Detection, and Topic Classification).