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With news and information being as easy to access as they currently are, itis more important than ever to ensure that people are not mislead by what theyread. Recently, the rise of neural fake news (AI-generated fake news) and itsdemonstrated effectiveness at fooling humans has prompted the development ofmodels to detect it. One such model is the Grover model, which can both detectneural fake news to prevent it, and generate it to demonstrate how a modelcould be misused to fool human readers. In this work we explore the Grovermodel's fake news detection capabilities by performing targeted attacks throughperturbations on input news articles. Through this we test Grover's resilienceto these adversarial attacks and expose some potential vulnerabilities whichshould be addressed in further iterations to ensure it can detect all types offake news accurately.

Exploring Semantic Perturbations on Grover