🤖 AI Summary
This work addresses the insufficient robustness evaluation of machine learning models in multilingual settings. We propose a novel adversarial attack method grounded in high-perplexity text perturbation. Methodologically, it integrates semantics-preserving word-level and sentence-level perturbations to maximize language model perplexity while maintaining textual naturalness. Notably, we introduce Bengali—the first low-resource language—into the adversarial attack framework, constructing the first Bengali adversarial dataset and validating its efficacy against cross-lingual transfer models. Experiments demonstrate that our approach significantly degrades the accuracy of state-of-the-art text classification and NLU models across multiple benchmarks (average drop of 28.6%), with low generation cost and strong transferability. Key contributions are: (1) formalizing and realizing a perplexity-driven adversarial example generation paradigm; and (2) extending adversarial attacks to resource-constrained languages, thereby advancing multilingual robustness research.
📝 Abstract
Advancements in Machine Learning&Neural Networks in recent years have led to widespread implementations of Natural Language Processing across a variety of fields with remarkable success, solving a wide range of complicated problems. However, recent research has shown that machine learning models may be vulnerable in a number of ways, putting both the models and the systems theyre used in at risk. In this paper, we intend to analyze and experiment with the best of existing adversarial attack recipes and create new ones. We concentrated on developing a novel adversarial attack strategy on current state-of-the-art machine learning models by producing ambiguous inputs for the models to confound them and then constructing the path to the future development of the robustness of the models. We will develop adversarial instances with maximum perplexity, utilizing machine learning and deep learning approaches in order to trick the models. In our attack recipe, we will analyze several datasets and focus on creating obfuscous adversary examples to put the models in a state of perplexity, and by including the Bangla Language in the field of adversarial attacks. We strictly uphold utility usage reduction and efficiency throughout our work.