Why an increasing validation loss and validation accuracy signifies overfitting?
Overfitting is when your model is memorizing the training data, and therefore it is not able to generalize to unseen data, like the validation set.
This way, you see overfitting when the training loss decreases but the validation loss increases.
The fact that, while the validation loss increases, the validation accuracy also increases is a separate effect. To understand it, we should take into account that the accuracy is computed comparing the highest output of the softmax with the correct label, and it does not vary depending on the actual value of the softmax output, which is what the validation loss takes into account.