  Although deep neural networks have achieved state-of-the-art performance in
various machine learning tasks, adversarial examples, constructed by adding
small non-random perturbations to correctly classified inputs, successfully
fool highly expressive deep classifiers into incorrect predictions. Approaches
to adversarial attacks in natural language tasks have boomed in the last five
years using character-level, word-level, phrase-level, or sentence-level
textual perturbations. While there is some work in NLP on defending against
such attacks through proactive methods, like adversarial training, there is to
our knowledge no effective general reactive approaches to defence via detection
of textual adversarial examples such as is found in the image processing
literature. In this paper, we propose two new reactive methods for NLP to fill
this gap, which unlike the few limited application baselines from NLP are based
entirely on distribution characteristics of learned representations: we adapt
one from the image processing literature (Local Intrinsic Dimensionality
(LID)), and propose a novel one (MultiDistance Representation Ensemble Method
(MDRE)). Adapted LID and MDRE obtain state-of-the-art results on
character-level, word-level, and phrase-level attacks on the IMDB dataset as
well as on the later two with respect to the MultiNLI dataset. For future
research, we publish our code.
