  We propose algorithms to create adversarial attacks to assess model
robustness in text classification problems. They can be used to create white
box attacks and black box attacks while at the same time preserving the
semantics and syntax of the original text. The attacks cause significant number
of flips in white-box setting and same rule based can be used in black-box
setting. In a black-box setting, the attacks created are able to reverse
decisions of transformer based architectures.
