Homograph Attacks on Maghreb Sentiment Analyzers
Fatima Zahra Qachfar Rakesh M. Verma
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.