If I got some generated data, and I build a model that distinguishes it from real data. What can I conclude?

I got a dataset with some real data and "generated" data with noise, etc.

I was curious as to how "good" was that data compared to the real data, so I thought that if I build a model, and the model is able to distinguish them apart with a high confidence, then there could be some clear bias and the probability distribution could be affected. And the extra data could not be helpful for the real problem.

With a simple …

build data dataset deeplearning etc generated good noise real data thought