This is true! Overfitting is definitely one of the biggest problems with deep learning. Some techniques to avoid it have been developed, such as dropout (introducing noise) and early stopping. But in general this is why deep learning requires huge amount of data, a deep learning model will overfit if not given enough data. That is also why (at this time) it only performs well for certain problems where the ratio between available data and problem complexity is high enough.