difference in accuracy between training and validation data to confidently detect overfitting

Question

I am working on a machine learning project and I want to know if there is a specific threshold for the difference in accuracy between training and validation data that can be used to confidently detect overfitting. I understand that overfitting occurs when a model performs well on the training data but poorly on the validation or test data. However, I am looking for a more concrete value or range that can be considered as a reliable indicator of overfitting. Can anyone provide a credible source or reference that addresses this question? Thank you in advance.