The proliferation of fake news poses significant challenges to both individuals and society. While numerous studies have developed detection models and datasets, these models often remain vulnerable to biases introduced by contaminated data sources or biased training methodologies. When trained on such data, machine learning models can unintentionally learn and propagate these biases, negatively impacting their generalization performance. Existing research on fake news detection has predominantly focused on political bias. This study addresses gender bias in fake news detection. While gender bias is well-documented in media, it remains largely unexamined in the context of fake news. Our investigation reveals that gender bias exists within fake news detection models, with models trained on female data outperforming those trained on male data. In response, we propose mitigating gender bias using an adversarial debaising approach. Our results demonstrate improved fairness and performance metrics compared to baseline models.