  In this paper, we introduce an enhanced textual adversarial attack method,
known as Saliency Attention and Semantic Similarity driven adversarial
Perturbation (SASSP). The proposed scheme is designed to improve the
effectiveness of contextual perturbations by integrating saliency, attention,
and semantic similarity. Traditional adversarial attack methods often struggle
to maintain semantic consistency and coherence while effectively deceiving
target models. Our proposed approach addresses these challenges by
incorporating a three-pronged strategy for word selection and perturbation.
First, we utilize a saliency-based word selection to prioritize words for
modification based on their importance to the model's prediction. Second,
attention mechanisms are employed to focus perturbations on contextually
significant words, enhancing the attack's efficacy. Finally, an advanced
semantic similarity-checking method is employed that includes embedding-based
similarity and paraphrase detection. By leveraging models like Sentence-BERT
for embedding similarity and fine-tuned paraphrase detection models from the
Sentence Transformers library, the scheme ensures that the perturbed text
remains contextually appropriate and semantically consistent with the original.
Empirical evaluations demonstrate that SASSP generates adversarial examples
that not only maintain high semantic fidelity but also effectively deceive
state-of-the-art natural language processing models. Moreover, in comparison to
the original scheme of contextual perturbation CLARE, SASSP has yielded a
higher attack success rate and lower word perturbation rate.
