Of course, he's the same roommate who later got arrested for wire fraud and grand theft.
It seemed smart, as it would give you the same four quarters if you used a Canadian Dollar or an American one. Or maybe not, as one kid found out when he stuck a $5 bill in and got 4 quarters.
Being 12, and therefore far more clever than the designers of this machine, the kids then proceeded to cut our a few thousand little rectangular strips of paper, roughly dollar sized, and clean that poor machine out.
And being 12, and having Ms PacMan machines as the only way of disposing of that giant sack of quarters, when it became apparent that there was no way to spend them fast enough, it naturally degenerated into a game of "Throw the quarter in the Ocean".
Out popped pound coins.
He now works for GCHQ which is funny.
Edit: just to add that these change machines were really naff and obviously built by the lowest bidder.
So he just moved on to even more serious crimes.
then I googled it and realized its british slang:'https://www.google.com/search?q=naff
It's the same with many words with the -ize suffix - we seem to have subconsciously accepted -ise to the point that spellcheckers demand it, when in actual fact the OED form of a word typically uses -ize.
Most British newspapers use the -ise form. The Oxford University Press use the -ize form. So it isn't as clear cut as you think.
I recall reading that the -ise alternatives have some roots in Australian us of the language.
DIP switches are set for various options one being accept face up (two-way), other options turn on or off acceptance of certain denomination bills and maintenance features such as calibration.
Even now modern acceptors allow this, although four-way acceptance is often preferred by the customer.
I'm embarassed to admit that in middle school my friend and I did something similar as his dad ran a printing business out of his garage. It surprised us then that this worked so easily. Luckily we only used them on vending machines and we quit doing it before we got caught. It makes me wonder what more sophisticated criminals can get away with.
A lot. http://en.wikipedia.org/wiki/Superdollar
They were exceptionally good, right down to the watermark, security strip, etc. etc. Even when I had a known real one and a known fake one, I couldn't tell them apart.
Even after they told me what to look for, I couldn't tell.
Tl;dr: 100$ bills produced in all likelihood by a foreign government (suspects are North Korea, Syria, Iran) or maybe criminal gangs are so well done that they are practically indistinguishable from the original.
The numbers printed seem to be pretty low though, and not nearly enough to endanger the economy in any way. Probably its hard to launder the money?
As far as counterfeit currency being a danger to economies, check out Operation Bernhard  where Nazi Germany forged British bank notes to flood the economy, with the goal of causing rapid inflation that would be damaging to the government.
There's a good movie as well, from 2007, about the operation: Die Fälsche (The Counterfeiters) . Definitely worth a watch.
This guy made beautifully artistic bills that didn't look official currency and then made purchases with them at their face value: $100, $500, etc.
Quality is a factor in the price so (for example) a bag of 100 £1 coins might cost £60 but you might not be able to use them in vending machines due to the weight being wrong. Notes again have their price depending on quality and cut.
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.
I.e. they are the best systems out there?
This phrasing is gold. "Our product is so good, the government buys it because it's better than the best thing out there." :)
What sort of features?
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.
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.
As much as I wish I could make a bet with you and test this, I wish for trouble from the secret service even less, so I guess that's off the table.
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.
PCA: Basically you represent your measurements as a covariance matrix about the data set you care about. You then find the eigenvalues and the eigenvectors of that matrix. These basically tell you the hyperplanes which most accurately represent your data sets. Unfortunately, I can't get into more details about how this is used for bill detection -- go read the patents and papers yourself.
SVM: Basically, you have a bunch of datasets, and you an unknown data point, and you want to figure out which dataset your new data point belongs to. Well, you're not a clever person, and neither am I, so you just come up with the "cloud that surrounds" your N-dimensional shapes. This is your Support Vector.
A Support Vector Machine is just "hey, I've got a bunch of characteristic datasets, find the minimum structure for each dataset that surrounds the cloud, and then let me compare them." In practice, it gets really thorny to find the minimum vector, so people use something called the Kernel Trick to simplify that into something more manageable. (Basically, it's a higher dimensional transform that maps your dataset into even higher dimensions which likely will simplify the data as there's probably an underlying structure to your data you don't know. You try a bunch of kernels, and take the one that works best for you.)
Again, I can't tell you how it relates to bill detection. I'm embargoed. Go look at the patents and papers yourself.
1) Features are everything
2) So is experience
When people buy machine learning experts they buy both of these things. Anyone can learn the math, it takes time to get good with it.
"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.
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.
Just as a simple example (stolen from the Caltech course which I highly recommend) if you look at points on a plane that form a circle and try to separate them with a line you're going to fail. I.e. your points are (x,y) and those in the circle are your fake dollar bill and those outside aren't. But you can take all these points and apply a non-linear transform, e.g. (x, y, xy, xxy, yyx, x+y, xx+y2), you get the idea... It turns out that now you can* separate the data into what's inside and outside the circle. The problem is you just increased your so called VC dimension of the model and you might overfit the data and not learn anything. SVMs let you get infinite numbers of combinations, without overfitting and with cheap calculations... Pretty neat.
A pretty picture cut to the right size would work. I think it was enough to prevent casual errors rather than strict validation.
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.
The majority of people will just take another bill out of their wallet and try again, so it is really a non-issue.
it just fatten someones pockets who then pass a percentage down to the policy makers.
The laundry/vending machines are an interesting case because there's a decent chance that the the vendors would just be taking bags of coins to the bank where, presumably, the Pesos would be kicked out correctly. Even then, if there were only a small number of pesos, they probably just passed them on at someone else's vending machine, or in other cash person-to-person transactions.
It's hilarious to me that, while you can argue about what currencies are backed by, at the end of the day all that matters is that something spends. If something spends like a quarter, it's a quarter.
I did verify experimentally that the Eurion constellation alone doesn't trigger photoshop's image rejection algorithm. I think it would be fun to distribute a bunch of images that false-positive the digimarc algorithms, just to mess with people.
I'm still undecided about whether countermeasures to reverse engineering like this are useful or not in the long run.
You can't possibly think that a criminal organization, with average tech resources, will have a hard time getting around this in a way or another.
But that's the whole point. If you can stop the idiots from copying notes, you have a lot fewer potential counterfeiters to deal with.
How do you mean? Is there much (legal) value to be had from image software that can process accurate scans of currency? I'm just kind of confused here, what value you speak of.
The Photoshop anti-counterfeiting stuff? It just scares off amateurs with an inkjet who don't realize every page from their printer has a tiny coded tracking mark that identifies the printer, date, and time.
Another argument for free software.
Unfortunately, they're printed all the time.
Could it be that each image generated from Adobe Software also tags the image with unique identifying computer, timestamp information?
Beyond that, I think the bigger threat to privacy by far in digital images is in EXIF data and reverse image searches.
As mentioned in this comment thread, there is a general description of the security features on newmoney.gov - while I was working on the original version of newmoney.gov for the release of the new $20 bill, we were given high DPI scans of the new bill and were only allowed to make low DPI, specific crops - all while in a "war room" that did not have internet access nor were we allowed to bring in any cameras or cellphones.
Some interesting little facts I was told while working with the BEP: most counterfeits are actually done by other countries with proper currency printing machines. Small time counterfeiting is generally bleached out $1 bills printed over with $5 images to retain the same cotton-paper ratio used so that it feels the same.
Something like: If you can see this: Call 1-800-FED
Here are a few Treasury Department webpages telling you what security measures are on the bill, and they publish a press release with a new interactive site when they change them:
The Secret Service will happily tell you about security features of currency, and behind closed doors help you build methods to detect counterfeits if you have a reasonable justification.
I would however think that they do that simply to protect themselves in case so that noone will come after them legally that they allowed counterfeit to happen (so easily). Sure you can always trick the machine/software, but hey at least Adobe did try to stop you so they do "work" in a good faith.
I once watched a documentary on counterfeits and it seems it would costs about $80 to produce a perfectly looking, no-difference $100 bill using the latest technology. Noone will ever attest its fake other than Federal Reserve, and thats only because they have never produced a note with this serial #. The real game, supposedly, is not in producing fake notes that pass all tests; the real "problem" is how you put enough number of bills in the circulation so that #1 you make plenty of money, and #2 you don't get noticed/caught.
Surprisingly, the price for a perfectly-fake bill is not going much higher than $80, and as the rumor goes, FedRe stopped producing $1,000 bills for this exact reason.
Slippery slope, this one.