They dont need to fine tune it for better results, they only need to fine tune it to give different results and sell it for $5 less than you are.

I misunderstood your point.

The "attack" that you are describing could probably be mitigated by doing activation analysis. Given random initialization even with fine-tuning the activation distribution will be similar. Same method should be used to weed out people just repackaging the ImageNet pretrained model from torchvision.

That being said you can still distill a network into another with a teacher-student approach and that would be undetectable.

I believe it has a kind of pre-moderation.

The marketplace should probably be on datasets.

I'd rather buy a model than a dataset that I have to spend money to train a model with.

You are limited in expanding your dataset and making your model better, if you only have the model. And I would rather see how clean/balanced the dataset my model was built from was.

When using models becomes more mainstream, I think it's better to strive for transparency than expandability.