Confusion matrix logic
A confusion matrix is a table that is often used to describe the performance of a classification model. The figure you have provided presents a binary case, but it is also used with more than 2 classes (there are just more rows/columns).

The rows refer to the actual Ground-Truth label/class of the input and the columns refer to the prediction provided by the model.

The name of the different cases are taken from the predictor's point of view.

True/False means that the prediction is the same as the ground truth and Negative/Positive refers to what was the prediction.

The 4 different cases in the confusion matrix:

True Positive (TP): The model's prediction is "Positive" and it is the same as the actual ground-truth class, which is "Positive", so this is a True Positive case.

False Negative (FN): The model's prediction is "Negative" and it is wrong because the actual ground-truth class is "Positive", so this is a False Negative case.

False Positive (FP): The model's prediction is "Positive" and it is wrong because the actual ground-truth class is "Negative", so this is a False Positive case.

True Negative (TN): The model's prediction is "Negative" and it is the same as the actual ground-truth class, which is "Negative", so this is a True Negative case.