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