Not quite. Another popular example is the cnn learning to distinguish wolves from non-wolves, because the wolf was always photographed on snow & the other animals not on snow. It just learnt to distinguish snow vs non-snow instead of wolf vs non-wolf. It then failed catastrophically when you put a non-wolf on snow.

There's no leakage in either case. The model is learning one thing and you think it's learning another.

No, it's called insufficient feature engineering. Data leakage is when your test data contaminates your training data.

Actually this is a clear case of overfitting leading to a high false positive rate resulting in the misidentification of a US ship for chinese.

I don't think that's a clear case of overfitting. You could have used a subset of the original data for training and the rest for validation and it would have generalised pretty well.

It doesn't generalise when the US ship is in Chinese waters, but that's because the system was never "learning" to recognize ships in the first place.