  Textual backdoor attack, as a novel attack model, has been shown to be
effective in adding a backdoor to the model during training. Defending against
such backdoor attacks has become urgent and important. In this paper, we
propose AttDef, an efficient attribution-based pipeline to defend against two
insertion-based poisoning attacks, BadNL and InSent. Specifically, we regard
the tokens with larger attribution scores as potential triggers since larger
attribution words contribute more to the false prediction results and therefore
are more likely to be poison triggers. Additionally, we further utilize an
external pre-trained language model to distinguish whether input is poisoned or
not. We show that our proposed method can generalize sufficiently well in two
common attack scenarios (poisoning training data and testing data), which
consistently improves previous methods. For instance, AttDef can successfully
mitigate both attacks with an average accuracy of 79.97% (56.59% up) and 48.34%
(3.99% up) under pre-training and post-training attack defense respectively,
achieving the new state-of-the-art performance on prediction recovery over four
benchmark datasets.
