How to use Autoencoders for outlier detection on images
If you use supervised learning you need to weighted you labels (here). 

Otherwise I strongly recommend to exclude every leaking image for a training dataset for an CAE. If you have the anomaly in your trainign dataset, your CAE will learn it as normal. This is actually an active research field, and not trivial for real datsets, but if you have a static image of the pipe, it should be easy. I would suggest that you use traditional CV for it and not an DL model, because you can interpret and fine tune the result.