  Adversarial attacks are a major challenge faced by current machine learning
research. These purposely crafted inputs fool even the most advanced models,
precluding their deployment in safety-critical applications. Extensive research
in computer vision has been carried to develop reliable defense strategies.
However, the same issue remains less explored in natural language processing.
Our work presents a model-agnostic detector of adversarial text examples. The
approach identifies patterns in the logits of the target classifier when
perturbing the input text. The proposed detector improves the current
state-of-the-art performance in recognizing adversarial inputs and exhibits
strong generalization capabilities across different NLP models, datasets, and
word-level attacks.
