Online fake news profoundly distorts public judgment and erodes trust in social platforms. While existing detectors achieve competitive performance on benchmark datasets, they remain notably vulnerable to malicious comments designed specifically to induce misclassification. This evolving threat landscape necessitates detection systems that simultaneously prioritize predictive accuracy and structural robustness. However, current detectors often fail to generalize across diverse and novel comment attack patterns. To bridge this gap, we propose AdComment, an adaptive adversarial training framework for robustness enhancement against diverse malicious comments. Based on cognitive psychology, we categorize adversarial comments into Fact Distortion, Logical Confusion, and Emotional Manipulation, and leverage LLMs to synthesize diverse, category-specific perturbations. Central to our framework is an InfoDirichlet Resampling (IDR) mechanism that dynamically adjusts malicious comment proportions during training, thereby steering optimization toward the model's most susceptible regions. Experimental results demonstrate that our approach achieves state-of-the-art performance on three benchmark datasets, improving the F1 scores by 17.9%, 14.5% and 9.0%, respectively.