The researchers noticed that if someone attempted to remove a tag from a product, it would slightly alter the glue with metal particles making the original signature slightly different. To counter this they trained a model:
The researchers produced a light-powered antitampering tag that is about 4 square millimeters in size. They also demonstrated a machine-learning model that helps detect tampering by identifying similar glue pattern fingerprints with more than 99 percent accuracy.
It’s a good use case for an ML model.
In my opinion, this should only be used for continuing to detect the product itself.
The danger that I can see with this product would be a decision made by management thinking that they can rely on this to detect tampering without considering other factors.
The use case provided in the article was for something like a car wash sticker placed on a customers car.
If the customer tried to peel it off and reattach it to a different car, the business could detect that as tampering.
However, in my opinion, there are a number of other reasons where this model could falsely accuse someone of tampering:
Temperature swings. A hot day could warp the glue/sticker slightly which would cause the antitampering device to go off the next time it’s scanned.
Having to get the windshield replaced because of damage/cracks. The customer would transfer the sticker and unknowingly void the sticker.
Kids, just don’t underestimate them.
In the end, most management won’t really understand this device well beyond statements like, “You can detect tampering with more than 99 percent accuracy!” And, unless they inform the customers of how the anti-tampering works, Customers won’t understand why they’re being accused of tampering with the sticker.