[68Hawk]

I would think that what is important in designing machine learning to detect anomalies such as re-coloring and trimming is the framing of the particular questions you are asking.

Wouldn't re-coloring involve the question: "Is all the ink laid down (non-autos) on the card of uniform droplet shape (or wet transfer or any other printing method) and 'age'? Even the saturation level (eg. height that the ink is sitting on the paper) would be different if you applied a modern ink to the stock that has a break in the surface to that which laid down at original printing.
Also It would be impossible to find ink of the same age as that used in a specific printing moment when the card sheets were first colored, if a 'red' spot luminesces (has different presentation qualities) differently to all the other red inks on the paper, you can be confident it was added at a different time.
No?

Re trimming, I'm imagining a similar question can be asked.
Whether that involves inspecting the side edge of the card's stock for a particular 'presentation' that a paper cutter leaves at its original operation, but which changes on any edge to receive a more recent cut...or some other important aspect which can universally be asked and requires a limited answer response for final judgement.

To the other aspects looking to be 'graded', I'm thinking also that it is the value of the question being asked to be machine learned that is of most importance.
I'm certainly not putting it past a hobby enthusiast or invested professional in the machine learning industry to be able to craft the kinds of questions that would lead to a satisfactory automated grading system.

Time frame? Who knows.