Homograph Attacks on Maghreb Sentiment Analyzers

production

social-sciences

Homograph attacks decrease Arabic sentiment analysis accuracy, highlighting weaknesses in language models.

Author

Fatima Zahra Qachfar, Rakesh M. Verma

Published

February 5, 2024

Homograph attacks have a significant impact on the Sentiment Analysis (SA) task of different Arabic dialects from the Maghreb North-African countries.
The 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 study aims to highlight the weaknesses of large language models (LLMs) and prioritize ethical and responsible Machine Learning.

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”.
Large language models (LLMs) are susceptible to malicious attacks such as homograph attacks, which pose significant challenges for LLMs that accept raw unfiltered input text.
The impact of character-based attacks on North-African languages is highlighted, emphasizing the importance of ethical and responsible machine learning.

The study focuses on the impact of homograph attacks on sentiment analyzers of North African dialects, but it does not delve into potential solutions or defense mechanisms against these attacks.
The research acknowledges the weaknesses of large language models (LLMs) but does not provide a comprehensive analysis of potential defense mechanisms or strategies to mitigate the impact of homograph attacks.
The study emphasizes the importance of ethical and responsible machine learning, but it does not provide concrete recommendations or guidelines for implementing ethical practices in the development of machine learning models.
The research is limited to highlighting the impact of homograph attacks on sentiment analysis, but it does not explore the broader implications of these attacks on other natural language processing tasks or applications.

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