A Black-box Adversarial Attack on Fake News Detection Systems

Abstract

The widespread diffusion of misinformation through digital platforms has raised significant concerns due to its adverse impacts on society and economy. Nowadays, the adoption of Artificial Intelligence and Machine Learning based mechanisms to automate fact checking processes and distinguish genuine from fake contents is mandatory. However, recent studies reveal vulnerabilities in AI models to adversarial attacks, where slight modifications of the input can deceive the classifiers. Adversarial Machine Learning strategies aim to compromise machine learning algorithms, posing challenges also for fake news detection models. This study focuses on the impact of adversarial attacks on fake news detection systems, utilizing a black-box attack approach against an unknown algorithm used by the online platforms. The research introduces a methodology leveraging a surrogate model to test the validity of malicious samples offline, with the aim of overcoming known limitations such as the high number of queries made to the target model.