  State-of-the-art text classification models are becoming increasingly reliant
on deep neural networks (DNNs). Due to their black-box nature, faithful and
robust explanation methods need to accompany classifiers for deployment in
real-life scenarios. However, it has been shown in vision applications that
explanation methods are susceptible to local, imperceptible perturbations that
can significantly alter the explanations without changing the predicted
classes. We show here that the existence of such perturbations extends to text
classifiers as well. Specifically, we introduceTextExplanationFooler (TEF), a
novel explanation attack algorithm that alters text input samples imperceptibly
so that the outcome of widely-used explanation methods changes considerably
while leaving classifier predictions unchanged. We evaluate the performance of
the attribution robustness estimation performance in TEF on five sequence
classification datasets, utilizing three DNN architectures and three
transformer architectures for each dataset. TEF can significantly decrease the
correlation between unchanged and perturbed input attributions, which shows
that all models and explanation methods are susceptible to TEF perturbations.
Moreover, we evaluate how the perturbations transfer to other model
architectures and attribution methods, and show that TEF perturbations are also
effective in scenarios where the target model and explanation method are
unknown. Finally, we introduce a semi-universal attack that is able to compute
fast, computationally light perturbations with no knowledge of the attacked
classifier nor explanation method. Overall, our work shows that explanations in
text classifiers are very fragile and users need to carefully address their
robustness before relying on them in critical applications.
