> In conclusion, I’m pretty negative about AI in general, but this experience made me cautiously optimistic about the role the technology could play in protecting consumers from counterfeit products.<p>Whaaaattt? How could the author go through such detail and discover that Gemini completely failed in the assigned task (calling all the products fakes when some were and some weren't means that it provided <i>zero</i> correct assistance in this discrimination task) and then conclude "AI could play a helpful role"??? Does the author also conclude that stopped clocks could play a helpful role telling time because the are correct twice a day?<p>Apparently AI isn't the only thing that hallucinates.
Replacement laptop batteries and power adapters have a huge problem with counterfeiting as well. In most cases it's a lot easier to spot because e.g. they rearrange the logos.<p>Even if these techniques did not produce false positives on genuine products, I suspect that they would not scale well. If counterfeiters realized they were losing a lot of sales through AI detection, they will just use AI to catch the errors themselves.
Regarding scaling - I think you're right if the AI mostly detects typos, like it did in this work.<p>I'm hopeful that future AI models (perhaps trained for this purpose, not a general-purpose model like I used here) might be able to identify more subtle variations in e.g. injection molding patterns, the surface finish of a pill, things that would be hopefully a lot harder for counterfeiters to fix than a typo.
This guy's lab does some really cool stuff using really basic ideas:<p>- <a href="https://groverlab.org/research/2022-05-06-candycodes.html" rel="nofollow">https://groverlab.org/research/2022-05-06-candycodes.html</a><p>- <a href="https://groverlab.org/research/2026-03-19-disintegration-fingerprinting.html" rel="nofollow">https://groverlab.org/research/2026-03-19-disintegration-fin...</a><p>(edit: Formatting)
It's baffling that the counterfeiters don't use the exact same design for the box. At least using the same text. Almost seems harder to make text thats sort of close but not just a straight copy.
Because they don’t have the source images. If they just used scans, the text would be fuzzy when reprinted. So they are converting it back to text in editing so it can print perfectly. Aside from some mistakes in the OCR.<p>As well as the fact they have already done well enough to fool almost all buyers.
The candycodes mentioned at the end of the article are absolutely brilliant as well. These products have extremely intimate relations with bodies and protecting that chain can avoid a lot of harm.
This is genius - I’ve had good look using AI to identify fakes (or just reproductions) in other areas. It’s surprisingly good, and works fast enough to be usable in a fast paced live auction environment.
This was a fun experiment but dang the formatting of this post was so hard to read.
This was not quite the way I expected to use AI