Article

Adversarial Text Generation with Dynamic Contextual Perturbation

2025-01-08

Abstract excerpt

Adversarial attacks on Natural Language Processing (NLP) models expose vulnerabilities by introducing subtle perturbations to input text, often leading to misclassification while maintaining human readability. Existing methods typically focus on word-level or local text segment alterations, overlooking the broader context, which results in detectable or semantically inconsistent perturbations. We propose a novel a...

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Literature Corpus work: 129b2c7b-585a-51bf-93aa-35956915728c
DOI: 10.20944/preprints202501.0643.v1

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