Defending against Insertion-based Textual Backdoor Attacks via Attribution. (arXiv:2305.02394v1 [cs.CL])

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 …

attack attacks attribution backdoor backdoor attacks important novel pipeline poisoning training urgent