  Research on adversarial attacks are becoming widely popular in the recent
years. One of the unexplored areas where prior research is lacking is the
effect of adversarial attacks on code-mixed data. Therefore, in the present
work, we have explained the first generalized framework on text perturbation to
attack code-mixed classification models in a black-box setting. We rely on
various perturbation techniques that preserve the semantic structures of the
sentences and also obscure the attacks from the perception of a human user. The
present methodology leverages the importance of a token to decide where to
attack by employing various perturbation strategies. We test our strategies on
various sentiment classification models trained on Bengali-English and
Hindi-English code-mixed datasets, and reduce their F1-scores by nearly 51 %
and 53 % respectively, which can be further reduced if a larger number of
tokens are perturbed in a given sentence.
