3 ms·

I got interested in this space a number of years ago, and have some investments in it; overall my takeaway is that supply chain integration is the main thing, n

I got interested in this space a number of years ago, and have some investments in it; overall my takeaway is that supply chain integration is the main thing, not necessarily hardware innovation, at least so far. Sadly, this is unlikely to be deployed commercially. But it's cool to come up with new ideas in the space! A couple key points: * Readers work at a 4cm distance * Rather than a key database, the researchers propose an AI to memorize patterns These are no-go for almost all product ID scenarios I can imagine. Not least because the AI will have a harder and harder time validating patterns the more patterns are thrown at it. As described, a registry would be better, much better. But still bad. David Chaum has done some work using glitter patterns on banknote bills, which I think is super cool, essentially: random reflective pattern from glitter -> some sort of FFT / visual identification signature -> stored in a registry. Even that is going to be somewhat tough because you'll need the registry. Or alternately a chip that can store a signature and a reference to the pattern, in which case you'll just need a reader that knows the public key of the signer.

YetAnotherNick 3y ago
Is this AI like detecting QR is an AI task or AI could actually mislabel product some percentage of time?
- toss1 3y ago
  I recall the article mentioned they got to "99% reliability". As a manufacturer of high-end products looking for a good authentication solution, I was thinking this might be cool until I read that. Having errors in 1 in 100 identification passes is nowhere near usable. I'd want I'd down under one misidentification in 10k or 100k readings (fail/redo/succeed is ok in 1%). The entire point of the system is to be able to definitely determine that "this is a counterfeit" or "yes, we made this in batch K69342". If it can't do that reliably, why would I invest in building this into our manufacturing flow?
  - YetAnotherNick 3y ago
    Reading the content it seems like 99% is only for detection of physical tampering, i.e taking the chip out of real thing and putting it in fake thing, which significantly decreases the intent tamper as you need to destroy the real thing.
    - toss1 3y ago
      Wow, if it's only 99% catching that condition, it seems even worse! (I read it as 99% re-recognizing un-mangled items, but yes, if it is 99999/100k re-recognizing a good tag, it might be ok to let pass 1/100 counterfeits).
bsza 3y ago
The way I understand it (or at least the way I'd design it), only the tag itself stores the pattern (or the AI-generated hash thereof), there is no external "registry". Then the pattern hash is used to access a private key (e. g. the key is stored XOR-ed with the hash when the tag is installed) to authenticate a challenge. Recognizing similar patterns is to make sure the pattern always evaluates to the same hash as long as it's not tampered with. The MIT link doesn't explain how exactly they've done it, so that's only speculation.
- nonrandomstring 3y ago
  The "hashing" presents something of a challenge. The input from a scan can be quite a lot of data, a high resolution photograph, a point cloud, a set of RF timings/phases or whatever. The key for future matching must be something small that can be distributed or sent ahead of the delivery, so a hash seems the way to go. But standard hash algorithms are designed for non-collision and sensitivity - any changes of input lead to wildly different outputs. But the physical product has to endure; - handling by humans or machines - mechanical vibration and stress - temperature variations - gravity, natural creep, thermoplasticity - bleaching due to light - variations in the reader, angle, lighting, nearby metal objects That means we need a threshold of fuzziness, where the hash is not broken, until suddenly it is. The more fuzzy we make it the more scope an attacker who knows the method has to craft a deliberate collision to repair tampering effects.
- vessenes 3y ago
  I understood it as: a signature of each pattern is stored as 'known' in a dataset. An AI learns which patterns are "known". I don't believe their method has fine control of placement of the 'readable' parts under the glue; this cuts out methods where you're writing something in data as part of authentication. But, it also makes production MUCH cheaper - you can later just read what you "wrote" under a tag. I would guess in the price points they imagine, this method is "random + read later". For your method - if you can write data, why not just write a digital signature of the item's serial number?
  - bsza 3y ago
greggsy 3y ago
I got a textbook that had a piece of lint embedded in a sticker. I don’t remember the actual authentication workflow, but I think the idea was to enter a code to a website, which would pull up a series of photos and you could select the right one, like a captcha.
rkagerer 3y ago
Are the readings not repeatable enough to derive a simple, secure key from the pattern? (I'm realtime of course - not suggesting caching it long term on the chip).
- deleted 3y ago
  [deleted]
wanderingstan 3y ago
What is the latest in this world? (Apart from this post) I developed some prototypes in the authentication space about ten years ago. Our approach was to detect fluorescence; first in IR, then shifted to Blue-to-Yellow shift that could be triggered by a mobile phone screen and simultaneously “read” by the front facing camera. The fluorescing dyes were trademarkable and very hard to develop, which we compared to holograms as markers of authenticity.
- vessenes 3y ago
  So, some tech like that is used in Banknotes, although the specifics are often covered under extremely strict NDAs. Companies that offer these NDAs around often offer product that watermarks in non-visible spectra, so you might infer they do something similar in the central banking space. It's a fairly patent-heavy field; I wouldn't be surprised if your work is covered at least in part by industry patents. If you have the patents still, I'd probably try and market them to companies like Kurz which have built large profitable businesses doing security layers for printed goods, among many other things. As you note, inks are a big deal, and control of inks and the ink supply chain can be a big deal. I put a bunch of money into Noteworthy a few years ago, which successfully embedded ARM chips into banknotes, the proposal being that you could at-note security with challenge/response over NFC. Noteworthy picked Bitcoin notes as a first product/demo tech. They work, it's very cool tech, and, shockingly, the chip embedding part of the tech stack isn't even the most expensive part of making a banknote. I also invested in a company looking at drug supply chains in low-data areas, which just used SMS-based single authentication checks (great idea -- didn't pan out), a company embedding chips in sneakers (great idea -- this one works, but it's not clear if it matters for mid-tier large issuance sneakers), ... I spent the most time with Noteworthy, and learned the banknote folks think of authentication in three tiers: individual (e.g. can you or a checkout person at a gas-station tell a note is fake), retail (e.g. can a bank teller tell a note is fake), and redacted, (e.g. can Treasury tell a note is fake). This multi-tier approach is practical and sensible to me; in brief, the farther up the chain, the more power you can bring to bear on determining a fake, and the longer you can take. I think the same dynamics are there in a lot of consumer goods -- and the upshot is that, at least in the states with consumer goods, people mostly want to / need to trust their supply chains; it's very rare anyone is going to even take a picture of something with their phone to check its authenticity.

YetAnotherNick 3y ago

Is this AI like detecting QR is an AI task or AI could actually mislabel product some percentage of time?

toss1 3y ago
I recall the article mentioned they got to "99% reliability". As a manufacturer of high-end products looking for a good authentication solution, I was thinking this might be cool until I read that. Having errors in 1 in 100 identification passes is nowhere near usable. I'd want I'd down under one misidentification in 10k or 100k readings (fail/redo/succeed is ok in 1%). The entire point of the system is to be able to definitely determine that "this is a counterfeit" or "yes, we made this in batch K69342". If it can't do that reliably, why would I invest in building this into our manufacturing flow?
- YetAnotherNick 3y ago
  Reading the content it seems like 99% is only for detection of physical tampering, i.e taking the chip out of real thing and putting it in fake thing, which significantly decreases the intent tamper as you need to destroy the real thing.
  - toss1 3y ago
    Wow, if it's only 99% catching that condition, it seems even worse! (I read it as 99% re-recognizing un-mangled items, but yes, if it is 99999/100k re-recognizing a good tag, it might be ok to let pass 1/100 counterfeits).

bsza 3y ago

The way I understand it (or at least the way I'd design it), only the tag itself stores the pattern (or the AI-generated hash thereof), there is no external "registry". Then the pattern hash is used to access a private key (e. g. the key is stored XOR-ed with the hash when the tag is installed) to authenticate a challenge. Recognizing similar patterns is to make sure the pattern always evaluates to the same hash as long as it's not tampered with. The MIT link doesn't explain how exactly they've done it, so that's only speculation.

nonrandomstring 3y ago
The "hashing" presents something of a challenge. The input from a scan can be quite a lot of data, a high resolution photograph, a point cloud, a set of RF timings/phases or whatever. The key for future matching must be something small that can be distributed or sent ahead of the delivery, so a hash seems the way to go. But standard hash algorithms are designed for non-collision and sensitivity - any changes of input lead to wildly different outputs. But the physical product has to endure; - handling by humans or machines - mechanical vibration and stress - temperature variations - gravity, natural creep, thermoplasticity - bleaching due to light - variations in the reader, angle, lighting, nearby metal objects That means we need a threshold of fuzziness, where the hash is not broken, until suddenly it is. The more fuzzy we make it the more scope an attacker who knows the method has to craft a deliberate collision to repair tampering effects.
vessenes 3y ago
I understood it as: a signature of each pattern is stored as 'known' in a dataset. An AI learns which patterns are "known". I don't believe their method has fine control of placement of the 'readable' parts under the glue; this cuts out methods where you're writing something in data as part of authentication. But, it also makes production MUCH cheaper - you can later just read what you "wrote" under a tag. I would guess in the price points they imagine, this method is "random + read later". For your method - if you can write data, why not just write a digital signature of the item's serial number?
- bsza 3y ago

greggsy 3y ago

I got a textbook that had a piece of lint embedded in a sticker. I don’t remember the actual authentication workflow, but I think the idea was to enter a code to a website, which would pull up a series of photos and you could select the right one, like a captcha.

rkagerer 3y ago

Are the readings not repeatable enough to derive a simple, secure key from the pattern? (I'm realtime of course - not suggesting caching it long term on the chip).

deleted 3y ago
[deleted]

wanderingstan 3y ago

What is the latest in this world? (Apart from this post) I developed some prototypes in the authentication space about ten years ago. Our approach was to detect fluorescence; first in IR, then shifted to Blue-to-Yellow shift that could be triggered by a mobile phone screen and simultaneously “read” by the front facing camera. The fluorescing dyes were trademarkable and very hard to develop, which we compared to holograms as markers of authenticity.

vessenes 3y ago
So, some tech like that is used in Banknotes, although the specifics are often covered under extremely strict NDAs. Companies that offer these NDAs around often offer product that watermarks in non-visible spectra, so you might infer they do something similar in the central banking space. It's a fairly patent-heavy field; I wouldn't be surprised if your work is covered at least in part by industry patents. If you have the patents still, I'd probably try and market them to companies like Kurz which have built large profitable businesses doing security layers for printed goods, among many other things. As you note, inks are a big deal, and control of inks and the ink supply chain can be a big deal. I put a bunch of money into Noteworthy a few years ago, which successfully embedded ARM chips into banknotes, the proposal being that you could at-note security with challenge/response over NFC. Noteworthy picked Bitcoin notes as a first product/demo tech. They work, it's very cool tech, and, shockingly, the chip embedding part of the tech stack isn't even the most expensive part of making a banknote. I also invested in a company looking at drug supply chains in low-data areas, which just used SMS-based single authentication checks (great idea -- didn't pan out), a company embedding chips in sneakers (great idea -- this one works, but it's not clear if it matters for mid-tier large issuance sneakers), ... I spent the most time with Noteworthy, and learned the banknote folks think of authentication in three tiers: individual (e.g. can you or a checkout person at a gas-station tell a note is fake), retail (e.g. can a bank teller tell a note is fake), and redacted, (e.g. can Treasury tell a note is fake). This multi-tier approach is practical and sensible to me; in brief, the farther up the chain, the more power you can bring to bear on determining a fake, and the longer you can take. I think the same dynamics are there in a lot of consumer goods -- and the upshot is that, at least in the states with consumer goods, people mostly want to / need to trust their supply chains; it's very rare anyone is going to even take a picture of something with their phone to check its authenticity.