Backdoor attacks have become an emerging threat to NLP systems. By providing
poisoned training data, the adversary can embed a ``backdoor'' into the victim
model, which allows input instances satisfying certain textual patterns (e.g.,
containing a keyword) to be predicted as a target label of the adversary's
choice. In this paper, we demonstrate that it's possible to design a backdoor
attack that is both stealthy (i.e., hard to notice) and effective (i.e., has a
high attack success rate). We propose BITE, a backdoor attack that poisons the
training data to establish strong correlations between the target label and
some ``trigger words'', by iteratively injecting them into target-label
instances through natural word-level perturbations. The poisoned training data
instruct the victim model to predict the target label on inputs containing
trigger words, forming the backdoor. Experiments on four medium-sized text
classification datasets show that BITE is significantly more effective than
baselines while maintaining decent stealthiness, raising alarm on the usage of
untrusted training data. We further propose a defense method named DeBITE based
on potential trigger word removal, which outperforms existing methods on
defending BITE and generalizes well to defending other backdoor attacks.

本文提出了一种名为 BITE 的后门攻击方法，通过注入包含 “触发词” 的训练数据，从而在模型中建立目标标签和触发词之间的强相关性，并形成后门，从而提高攻击成功率。作者还提出了一种名为 DeBITE 的防御方法，能够有效抵御后门攻击。

迭代触发器注入的文本后门攻击

BITE: Textual Backdoor Attacks with Iterative Trigger Injection

Event detection (ED) aims at detecting event trigger words in sentences and
classifying them into specific event types. In real-world applications, ED
typically does not have sufficient labelled data, thus can be formulated as a
few-shot learning problem. To tackle the issue of low sample diversity in
few-shot ED, we propose a novel knowledge-based few-shot event detection method
which uses a definition-based encoder to introduce external event knowledge as
the knowledge prior of event types. Furthermore, as external knowledge
typically provides limited and imperfect coverage of event types, we introduce
an adaptive knowledge-enhanced Bayesian meta-learning method to dynamically
adjust the knowledge prior of event types. Experiments show our method
consistently and substantially outperforms a number of baselines by at least 15
absolute F1 points under the same few-shot settings.

提出知识为基础的少样本事件检测方法来解决在实际应用中缺乏正确标记数据的问题，并引入自适应知识增强贝叶斯元学习方法来动态地调整事件类型的知识先验。实验结果表明，在相同的少样本条件下，该方法在至少 15 个绝对 F1 点上一致且显著地优于多个基准线。