The Universal Trigger (UniTrigger) is a recently-proposed powerful
adversarial textual attack method. Utilizing a learning-based mechanism,
UniTrigger generates a fixed phrase that, when added to any benign inputs, can
drop the prediction accuracy of a textual neural network (NN) model to near
zero on a target class. To defend against this attack that can cause
significant harm, in this paper, we borrow the "honeypot" concept from the
cybersecurity community and propose DARCY, a honeypot-based defense framework
against UniTrigger. DARCY greedily searches and injects multiple trapdoors into
an NN model to "bait and catch" potential attacks. Through comprehensive
experiments across four public datasets, we show that DARCY detects
UniTrigger's adversarial attacks with up to 99% TPR and less than 2% FPR in
most cases, while maintaining the prediction accuracy (in F1) for clean inputs
within a 1% margin. We also demonstrate that DARCY with multiple trapdoors is
also robust to a diverse set of attack scenarios with attackers' varying levels
of knowledge and skills. Source code will be released upon the acceptance of
this paper.

本篇论文提出了一种基于引诱陷阱的防御机制 DARCY，用于对抗最近提出的文本对抗攻击方法 Universal Trigger (简称 UniTrigger)。通过向神经网络模型注入多个引诱陷阱，DARCY 能够在多个公共数据集上检测到 UniTrigger 的攻击，并保持 1% 内的清洁输入预测准确性。