  Social media platforms are deploying machine learning based offensive
language classification systems to combat hateful, racist, and other forms of
offensive speech at scale. However, despite their real-world deployment, we do
not yet comprehensively understand the extent to which offensive language
classifiers are robust against adversarial attacks. Prior work in this space is
limited to studying robustness of offensive language classifiers against
primitive attacks such as misspellings and extraneous spaces. To address this
gap, we systematically analyze the robustness of state-of-the-art offensive
language classifiers against more crafty adversarial attacks that leverage
greedy- and attention-based word selection and context-aware embeddings for
word replacement. Our results on multiple datasets show that these crafty
adversarial attacks can degrade the accuracy of offensive language classifiers
by more than 50% while also being able to preserve the readability and meaning
of the modified text.
