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arXiv · 2402.03171

Homograph Attacks on Maghreb Sentiment Analyzers

Fatima Zahra Qachfar·Rakesh M. Verma
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Abstract

We examine the impact of homograph attacks on the Sentiment Analysis (SA) task of different Arabic dialects from the Maghreb North-African countries. Homograph attacks result in a 65.3% decrease in transformer classification from an F1-score of 0.95 to 0.33 when data is written in "Arabizi". The goal of this study is to highlight LLMs weaknesses' and to prioritize ethical and responsible Machine Learning.

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Fatima Zahra Qachfar, Rakesh M. Verma. 2024-02-05. Homograph Attacks on Maghreb Sentiment Analyzers. https://arxiv.org/abs/2402.03171

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