  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.
