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.
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.
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.
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.
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.
As well as the fact they have already done well enough to fool almost all buyers.
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.
That's fascinating! If any of your work using AI to identify reproductions is publicly sharable, I'd love to learn more about it (amateur antique collector here). But if not I understand.
> 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.
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 zero 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?
Apparently AI isn't the only thing that hallucinates.
That was my thought too. It failed amazingly at the task yet they were still thinking it was helpful. I had to go back and reread bits because I thought I’d missed a bit where Gemini hadn’t cocked up.
Very odd conclusion to an otherwise interesting experiment.
Perhaps a softer critique would be that the author seems to assume that (A) feature-detection will map to (B) accurate and useful conclusions, and that isn't necessarily true.
This guy's lab does some really cool stuff using really basic ideas:
- https://groverlab.org/research/2022-05-06-candycodes.html
- https://groverlab.org/research/2026-03-19-disintegration-fin...
(edit: Formatting)
Thanks! I'm putting "doing cool stuff with really basic ideas" in my bio... :)
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.
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.
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.
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.
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.
As well as the fact they have already done well enough to fool almost all buyers.
Any competent graphic designer can do this. I think it’s a lack of effort because… they don’t need to spend the extra $100.
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.
That's fascinating! If any of your work using AI to identify reproductions is publicly sharable, I'd love to learn more about it (amateur antique collector here). But if not I understand.
> 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.
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 zero 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?
Apparently AI isn't the only thing that hallucinates.
That was my thought too. It failed amazingly at the task yet they were still thinking it was helpful. I had to go back and reread bits because I thought I’d missed a bit where Gemini hadn’t cocked up.
Very odd conclusion to an otherwise interesting experiment.
Perhaps a softer critique would be that the author seems to assume that (A) feature-detection will map to (B) accurate and useful conclusions, and that isn't necessarily true.
This was a fun experiment but dang the formatting of this post was so hard to read.
Thanks, this is how we should use AI, not creating useless slop in a hope to get karma and stars.
This was not quite the way I expected to use AI