A Modified Word Saliency-Based Adversarial Attack on Text Classification
  Models
Hetvi Waghela Sneha Rakshit Jaydip Sen
This paper introduces a novel adversarial attack method targeting text
classification models, termed the Modified Word Saliency-based Adversarial
At-tack (MWSAA). The technique builds upon the concept of word saliency to
strategically perturb input texts, aiming to mislead classification models
while preserving semantic coherence. By refining the traditional adversarial
attack approach, MWSAA significantly enhances its efficacy in evading detection
by classification systems. The methodology involves first identifying salient
words in the input text through a saliency estimation process, which
prioritizes words most influential to the model's decision-making process.
Subsequently, these salient words are subjected to carefully crafted
modifications, guided by semantic similarity metrics to ensure that the altered
text remains coherent and retains its original meaning. Empirical evaluations
conducted on diverse text classification datasets demonstrate the effectiveness
of the proposed method in generating adversarial examples capable of
successfully deceiving state-of-the-art classification models. Comparative
analyses with existing adversarial attack techniques further indicate the
superiority of the proposed approach in terms of both attack success rate and
preservation of text coherence.