9 ms·

Given the cost of high quality ink and paper, one wonders how much less than $1 those bills cost to make.

aaronem 13y ago
Given that it was a decade and a half ago, and that even today's vending machine recognizers probably don't do much beyond making sure a few relatively gross features are present, I doubt the cost per counterfeit was all that close to $1.
- minikites 13y ago
  "The primary method vending machines use to recognize the denomination of paper money is through a magnetic scan; paper currency is printed with magnetic ink, similar to the ink on the MICR line of a check, that makes it easily identifiable to machines with magnetic scanners. In addition, each denomination is marked with different fluorescent properties. Many vending machines and other machines that read paper currency use an ultraviolet light to scan the bill, read the fluorescent response and issue the appropriate credit."
  - aaronem 13y ago
    Presumably they were simpler-minded fifteen years ago; either that, or the art major's high-end printer was a lot higher-end than it seemed.
    - minikites 13y ago
      Or a third option: the story is fiction.
      - aaronem 13y ago
        Well, you'll look at it how you please, and I'll do likewise.
    - thirdsight 13y ago
      Actually the cheap ones were really naff and relied on correctly sized notes and a mask and backlight with a couple of phototransistors. A pretty picture cut to the right size would work. I think it was enough to prevent casual errors rather than strict validation.
      - nappy-doo 13y ago
        These are Japanese machines. The US and Swiss makers are optical. There's been consolidation in the industry, so there's really only the US and JAP makers left, and the US maker (MEI) is so far ahead of the rest of the world, there's no point.
  - nappy-doo 13y ago
    Incorrect. The magnetic detectors haven't been manufactured or sold since the early 80s. The simplest machines do use 2 narrow optical detectors, but as their algorithms are considered good enough that you have to basically destroy a bill to use them, what's the point. See my other post about SVM/PCA in this thread.
    - minikites 13y ago
      What is SVM and PCA? That sounds interesting.
      - nappy-doo 13y ago
        Support Vector Machines, and Principle Component Analysis. LMGTFY
        minikites 13y ago
        Wow, that helps. "Principal components are linear combinations of original variables x1, x2, etc. So when you do SVM on PCA decomposition you work with these combinations instead of original variables." "What do you do to the data? My answer: nothing. SVMs are designed to handle high-dimensional data. I'm working on a research problem right now that involves supervised classification using SVMs. Along with finding sources on the Internet, I did my own experiments on the impact of dimensionality reduction prior to classification. Preprocessing the features using PCA/LDA did not significantly increase classification accuracy of the SVM." I can see how that relates to currency detection.
        bunderbunder 13y ago
        I'm an amateur in machine learning myself so I don't have a lot of knowledge of the details, but allow me to take a guess at what it's about. Support vector machines are a machine learning algorithm that works by taking data points in some (usually) high-dimensional space, and classifies them based on where they lie in relation to a boundary that (mostly) divides the positive examples from the negative ones. So one way a bill detection SVM might work is by using images of the bills are being transformed into points in that high-dimensional space by treating individual pixels as different dimensions, and deciding if they're valid banknotes (and the denomination) based on where in that space a given point falls. Since SVMs are designed to work well in high-dimensional data, you're correct that principal component analysis doesn't normally help them do better. Oftentimes it makes them perform worse. More likely, the reason they're using doing dimensionality reduction is to cut down on the size of the SVM's model. That could help in two ways: If you're using a really massive number of training examples, then dimensionality reduction can help cut down on the time it takes to train the SVM, or the space you need to store your training set. And if you're trying to fit the SVM into an embedded system, then dimensionality reduction would allow you to produce an SVM that runs well on lower-cost hardware.
  - rdtsc 13y ago
    Hmm what if the strip was damaged or say brushed against a magnet at some point. Would it reject those bills? Are they still valid bills even with a damaged magnetic signature?
    - dsl 13y ago
      Ever had a perfectly crisp dollar bill that a vending machine would just refuse to take? The majority of people will just take another bill out of their wallet and try again, so it is really a non-issue.
- nappy-doo 13y ago
  You are absolutely, positively wrong on this front. The new bill detectors use PCA and SVM to analyze notes. They're so good the treasury department buys them because they're better than the best systems out there. Generally, however, the vending business is quite cheap. They buy the low end machine because there's not really a reason to bother buying the more expensive ones. However, go to a casino, or a foreign country that uses large bills, and you'll find the story is totally different. The machines are incredibly good. * Source: I worked in this industry for years.
  - deleted 13y ago
    [deleted]
  - abrichr 13y ago
    The new bill detectors use PCA and SVM to analyze notes. What sort of features?
    - jjoonathan 13y ago
      You're right, that's the critical question. SVM on a picture of the note would be useless against all but the 12 year olds with paper and scissors. There's no reason to believe that the differentiating features from next years' printers would be separated by the same support vectors. I think the real security is still in features that require expensive equipment to duplicate. Is it really that hard to use cheapo photodetectors to verify differential transmission/reflection (watermark), angle-dependent coloration (hologram), or to do some primitive spectroscopy (UV even) with a plastic lens and $10 CCD? SVM might be a easy way to aggregate the features, but in that case it's just a calibration method and doesn't give any indication of the underlying security.
      - nappy-doo 13y ago
        And this is where you're wrong. Most detectors use a PIN diode or a phototransistor. Both work just fine with the SVM. Again, the security given in these detectors is SO good that even if I were to give you complete knowledge of the system, you can't beat them. I can't admit to having made counterfeits, but I can say that I've seen _all_ of them, and they do not work.
midas007 13y ago
It's likely an economy-of-scale: bigger the operation, the better the ROI. Low volume would be expensive.

Given that it was a decade and a half ago, and that even today's vending machine recognizers probably don't do much beyond making sure a few relatively gross features are present, I doubt the cost per counterfeit was all that close to $1.

minikites 13y ago
"The primary method vending machines use to recognize the denomination of paper money is through a magnetic scan; paper currency is printed with magnetic ink, similar to the ink on the MICR line of a check, that makes it easily identifiable to machines with magnetic scanners. In addition, each denomination is marked with different fluorescent properties. Many vending machines and other machines that read paper currency use an ultraviolet light to scan the bill, read the fluorescent response and issue the appropriate credit."
- aaronem 13y ago
  Presumably they were simpler-minded fifteen years ago; either that, or the art major's high-end printer was a lot higher-end than it seemed.
  - minikites 13y ago
    Or a third option: the story is fiction.
    - aaronem 13y ago
      Well, you'll look at it how you please, and I'll do likewise.
  - thirdsight 13y ago
    Actually the cheap ones were really naff and relied on correctly sized notes and a mask and backlight with a couple of phototransistors. A pretty picture cut to the right size would work. I think it was enough to prevent casual errors rather than strict validation.
    - nappy-doo 13y ago
      These are Japanese machines. The US and Swiss makers are optical. There's been consolidation in the industry, so there's really only the US and JAP makers left, and the US maker (MEI) is so far ahead of the rest of the world, there's no point.
- nappy-doo 13y ago
  Incorrect. The magnetic detectors haven't been manufactured or sold since the early 80s. The simplest machines do use 2 narrow optical detectors, but as their algorithms are considered good enough that you have to basically destroy a bill to use them, what's the point. See my other post about SVM/PCA in this thread.
  - minikites 13y ago
    What is SVM and PCA? That sounds interesting.
    - nappy-doo 13y ago
      Support Vector Machines, and Principle Component Analysis. LMGTFY
      - minikites 13y ago
        Wow, that helps. "Principal components are linear combinations of original variables x1, x2, etc. So when you do SVM on PCA decomposition you work with these combinations instead of original variables." "What do you do to the data? My answer: nothing. SVMs are designed to handle high-dimensional data. I'm working on a research problem right now that involves supervised classification using SVMs. Along with finding sources on the Internet, I did my own experiments on the impact of dimensionality reduction prior to classification. Preprocessing the features using PCA/LDA did not significantly increase classification accuracy of the SVM." I can see how that relates to currency detection.
        bunderbunder 13y ago
        I'm an amateur in machine learning myself so I don't have a lot of knowledge of the details, but allow me to take a guess at what it's about. Support vector machines are a machine learning algorithm that works by taking data points in some (usually) high-dimensional space, and classifies them based on where they lie in relation to a boundary that (mostly) divides the positive examples from the negative ones. So one way a bill detection SVM might work is by using images of the bills are being transformed into points in that high-dimensional space by treating individual pixels as different dimensions, and deciding if they're valid banknotes (and the denomination) based on where in that space a given point falls. Since SVMs are designed to work well in high-dimensional data, you're correct that principal component analysis doesn't normally help them do better. Oftentimes it makes them perform worse. More likely, the reason they're using doing dimensionality reduction is to cut down on the size of the SVM's model. That could help in two ways: If you're using a really massive number of training examples, then dimensionality reduction can help cut down on the time it takes to train the SVM, or the space you need to store your training set. And if you're trying to fit the SVM into an embedded system, then dimensionality reduction would allow you to produce an SVM that runs well on lower-cost hardware.
- rdtsc 13y ago
  Hmm what if the strip was damaged or say brushed against a magnet at some point. Would it reject those bills? Are they still valid bills even with a damaged magnetic signature?
  - dsl 13y ago
    Ever had a perfectly crisp dollar bill that a vending machine would just refuse to take? The majority of people will just take another bill out of their wallet and try again, so it is really a non-issue.
nappy-doo 13y ago
You are absolutely, positively wrong on this front. The new bill detectors use PCA and SVM to analyze notes. They're so good the treasury department buys them because they're better than the best systems out there. Generally, however, the vending business is quite cheap. They buy the low end machine because there's not really a reason to bother buying the more expensive ones. However, go to a casino, or a foreign country that uses large bills, and you'll find the story is totally different. The machines are incredibly good. * Source: I worked in this industry for years.
- deleted 13y ago
  [deleted]
- abrichr 13y ago
  The new bill detectors use PCA and SVM to analyze notes. What sort of features?
  - jjoonathan 13y ago
    You're right, that's the critical question. SVM on a picture of the note would be useless against all but the 12 year olds with paper and scissors. There's no reason to believe that the differentiating features from next years' printers would be separated by the same support vectors. I think the real security is still in features that require expensive equipment to duplicate. Is it really that hard to use cheapo photodetectors to verify differential transmission/reflection (watermark), angle-dependent coloration (hologram), or to do some primitive spectroscopy (UV even) with a plastic lens and $10 CCD? SVM might be a easy way to aggregate the features, but in that case it's just a calibration method and doesn't give any indication of the underlying security.
    - nappy-doo 13y ago
      And this is where you're wrong. Most detectors use a PIN diode or a phototransistor. Both work just fine with the SVM. Again, the security given in these detectors is SO good that even if I were to give you complete knowledge of the system, you can't beat them. I can't admit to having made counterfeits, but I can say that I've seen _all_ of them, and they do not work.

midas007 13y ago

It's likely an economy-of-scale: bigger the operation, the better the ROI. Low volume would be expensive.