Bridge the Gap Between CV and NLP! An Optimization-based Textual Adversarial Attack Framework. (arXiv:2110.15317v3 [cs.CL] UPDATED)

Despite recent success on various tasks, deep learning techniques still
perform poorly on adversarial examples with small perturbations. While
optimization-based methods for adversarial attacks are well-explored in the
field of computer vision, it is impractical to directly apply them in natural
language processing due to the discrete nature of the text. To address the
problem, we propose a unified framework to extend the existing
optimization-based adversarial attack methods in the vision domain to craft
textual adversarial samples. In this framework, …

address adversarial adversarial attacks attack attacks bridge computer computer vision deep learning framework gap language natural language natural language processing nature nlp optimization problem techniques text