Last week, my wife who grew up in a hilltribe away from the modern world was using Gemini for this exactly. She found out that some face cream she bought was fake. I've been working with AI for the last 8 years. I didn't want to pollute her mind with AI and tech because I love the way she walks around thinking about fruit and trees and nature but she learned how to use AI by herself. She uses it for learning how to do things in "the city" like booking airplanes and hotels and asks Gemini questions all the time.
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.
I think my "cautious optimism" mostly came from the fact that Gemini found subtle problems with the counterfeit packages that I (someone who looks at fakes as part of my job) didn't notice - like mismatched info between the tube and box, and a malformed Irish postal code. If I didn't notice those, then I seriously doubt that a consumer glancing at a package would notice them. In that case, Gemini's insights would be valuable info for a consumer.
Gemini wrongly called an authentic product a fake, but that was mainly because there really were typographical errors in the authentic product's ingredient list. That's more of an indictment of the manufacturer than it is Gemini.
Finally, a lot of the things that Gemini got wrong seemed to me like they could reasonably be attributed to things like optical artifacts in the photos (glare, shadows, stuff like that). Better/more photos might improve that.
All that being said, this is obviously a tiny study of a single AI tool with a single brand of cosmetic product, so I should probably withhold my "cautious optimism" until we have more data.
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.
Counterfeit makers are also going to start using AI to find problems and improve their offering.
Last week, my wife who grew up in a hilltribe away from the modern world was using Gemini for this exactly. She found out that some face cream she bought was fake. I've been working with AI for the last 8 years. I didn't want to pollute her mind with AI and tech because I love the way she walks around thinking about fruit and trees and nature but she learned how to use AI by herself. She uses it for learning how to do things in "the city" like booking airplanes and hotels and asks Gemini questions all the time.
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.
I think my "cautious optimism" mostly came from the fact that Gemini found subtle problems with the counterfeit packages that I (someone who looks at fakes as part of my job) didn't notice - like mismatched info between the tube and box, and a malformed Irish postal code. If I didn't notice those, then I seriously doubt that a consumer glancing at a package would notice them. In that case, Gemini's insights would be valuable info for a consumer.
Gemini wrongly called an authentic product a fake, but that was mainly because there really were typographical errors in the authentic product's ingredient list. That's more of an indictment of the manufacturer than it is Gemini.
Finally, a lot of the things that Gemini got wrong seemed to me like they could reasonably be attributed to things like optical artifacts in the photos (glare, shadows, stuff like that). Better/more photos might improve that.
All that being said, this is obviously a tiny study of a single AI tool with a single brand of cosmetic product, so I should probably withhold my "cautious optimism" until we have more data.
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.
This was not quite the way I expected to use AI
Thanks, this is how we should use AI, not creating useless slop in a hope to get karma and stars.