Robustness of models addressing Information Disorder: a comprehensive review and benchmarking study

Machine learning and deep learning models are increasingly susceptible to adversarial attacks, particularly in critical areas like cybersecurity and Information Disorder. This study provides a comprehensive evaluation of model Robustness against such attacks across key tasks well-assessed in Information Disorder literature: Toxic Speech Detection, Sentiment Analysis, Propaganda Detection, and Hate Speech Detection. Rigorous experiments conducted across 13 models and 12 diverse datasets highlight significant vulnerabilities. The methodological framework implements adversarial attacks that strategically manipulates model inputs based on keyword significance, identified using the LIME method, an advanced explainable AI technique. The evaluation measures Robustness primarily through accuracy of the models and attack success rates. The experiments reveal that current models display inconsistent resistance to adversarial manipulations, underscoring an urgent need for developing more sophisticated defensive strategies. The study sheds light on the critical weaknesses in existing models and charts a course for future research to fortify AI resilience against evolving cyber threats. The findings advocate for a paradigm shift in model training and development to prioritize adversarial Robustness, ensuring that AI systems are equipped to handle real-world adversarial scenarios effectively.