  The spread of fake news has emerged as a critical challenge, undermining
trust and posing threats to society. In the era of Large Language Models
(LLMs), the capability to generate believable fake content has intensified
these concerns. In this study, we present a novel paradigm to evaluate fake
news detectors in scenarios involving both human-written and LLM-generated
misinformation. Intriguingly, our findings reveal a significant bias in many
existing detectors: they are more prone to flagging LLM-generated content as
fake news while often misclassifying human-written fake news as genuine. This
unexpected bias appears to arise from distinct linguistic patterns inherent to
LLM outputs. To address this, we introduce a mitigation strategy that leverages
adversarial training with LLM-paraphrased genuine news. The resulting model
yielded marked improvements in detection accuracy for both human and
LLM-generated news. To further catalyze research in this domain, we release two
comprehensive datasets, \texttt{GossipCop++} and \texttt{PolitiFact++}, thus
amalgamating human-validated articles with LLM-generated fake and real news.
