>> POLLARD: So this paper's in two parts, so it's a short paper because it's a continuation
of some existing work and the existing work is on print forensics.
And the particular bit we're doing here is a study looking at how the substrate affects
this, so the sub--the paper type we're using.
And so, first of all, I'll do introduction to the forensics and then I'll get back to
the particular experiments we--we've done here.
So this is doing work with Guy Adams, who's a Harvard Engineer and who's responsible for
the hardware, and also collected all the data for these experiments which was a considerable
task and then the image processing, encoding, which is a--was the responsibility of the
two Stevens and I'm the Steven in the middle.
So, people may be familiar with Printer Forensics, where you try to do--if you get some document,
you want to know which printer it was printed on but what we're looking at is Print Forensics,
where we want to identify individual printed items.
So every single thing you print, if you look at it in enough detail, it has its own unique
fingerprint and we can really robustly identify each and every printed item, so we show here
with these high-resolution images, if you really look at--this is particularly bad because
this is something very small inkjet characters in a six point text but if you look at those,
you can see that each of them has some you know, a kind of--some considerable differences
which are due to they way the ink splatters and the substrate you're printing on, and
this true of any print type, you know, across the board, everything, if you look at it in
enough detail, there's enough there to get a unique image of it, a unique representation
of it.
Conversely, we look at this with a low-resolution scanner, everything gets blurred away, so
the image on the right here is the same as the image in the center and put--it looks--you
know, you can't get all those features from it.
So essentially want to be able to do that, so to be able to do--to be able to identify
things uniquely, we need some low cost hardware to do this so that we could--put with every
printer and we need some image processing.
So the hardware is based on the realization that any cents you buy these days, the sticking
mobile phone, is actually really high resolution.
And if we--we're able to use just one to one optics, we would have the resolution we require,
so it's just like a much more, if we do--this provides--if we were able to do it in a contact
fashion, we don't have any problems with that field, so the hardware we used is based on,
something called Dyson Relay Lens, which is a very low cost way of achieving one to one
optics in a contact image fashion, so to show--so this is the device we've built here, which
was built by Guy Adams and he uses this, you know simple low cost mobile phone sensor.
We look at the optics themselves--what these optics were designed for is for replicating
things and they're using the semi conductor industry.
Basically all this consist of this is a single refractive surface and a mirror, it allows
you to replicate an object by an image as we can see on the top situation, all we add
here is a--we add a sensor off to the left or down to the bottom and introduce some illumination
device, so it's a slightly more complicated situation than usual but it's still low cost
and very simple to make.
So based on this, we can do these very high resolution images, so then we need some really
robust image processing that enables us to get this stuff easily and those people who
came along to the demo the other day will have seen that, you know that--it's--it does
gives us exceedingly high confidence robust imaging.
And we're able to robustly identify every single printed item uniquely, as long as we
take one--the idea is you take one image of it as you print it and store it on a data
base, and later we can compare any subsequent image of it and identify them uniquely.
And the way to do this, is by extracting some information based on--a model based approached,
so the idea is we have a reference model of the thing we want to print and what we wanted
to identify is how the thing we actually printed differs from the reference model, so the reference
model just consists of a set of points that identify--that represent the outline of the
thing you're going to print, so anything can be used as a forensic mark, in this case,
we're going to use the letter A and we're going to use the outline of the letter A to
provide a model, so that we can robustly identify it, and the model here, we're going to show
a hundred points, the model will actually consist of several thousand points, two thousand
in the examples I'm showing here and we also, for every point, have pre compute, it's normal,
so that we know, once we've locate the model, what the normal direction is.
So, going back to the--to how this method works, so we identify--we have this model,
we take an image with a high resolution imaging device, we locate the instance of the forensic
mark that we're going to use, it this case the letter A and--because it's a model--this
based approach gives us a very high confidence and very well located registration of our
outline--on the actual image from that, we're able to identify how this particular letter
A differs from every other Letter A that was printed.
And the way we do this, is use the normal information to extract a border image around
the--around the perimeter of this particular item, from that we extract a signature profile
which is a representation of that border.
The way that's done is the sort of General Edge Based approach but it uses all the edge
information that's orthogonal to the printed item, so we compute some edge strengths and
then we combine those of--using the windowing function, I mean the details aren't important,
they are in the paper but the ideas is we can work with this unique representation,
which is a model based signature profile for any particular representation of the character.
Now that's still quite a lot more information but we have--and we found we can actually
reduce that enormously and come up with a simple coding scheme where we get down to
a few hundred integer values, the way we do that is to--we chunk this very high resolution
working descriptor and we measure variances that--on these chunks.
In this case, we have 50 chunks across our shape and we measure variances and then we
come up with a mean variance.
We use that as a coding parameter to come down to a very small number of bits at the
end of the day.
And to show how this is effective, we--here we have two different signature profiles extracted
from two captures of the same piece of text, the same text items and same letter A. And
we extract two separate codings, the difference between those codings gives us something we
call the shape distortion encoding distance.
As you can see that's a--in this case it comes out with the value .22 as we'll see latent.
That's quite a--that's a low value in comparison to what we get when we compare two different
letter As.
So, the sort of experiments we do with this--with this system is we take a large number of images
with one docs or device, and then another set of images with another docs or device.
But where each--for each one we've got, you know, we have corresponding pairs of images
of the same character.
In this case, we have 36 instances, but most of the experiments I'm going to show here,
we collect 40 images of each letter.
So, when we look at the difference between correct and false matches here, we see that
the correct matches all have, you know, rather low STED values.
And our incorrect matches are well separated from them, and these two distributions are
very well separated which allows us to differentiate, correct and verify instances.
If we look how this parameter--as we change the number of elements that are used to represent
STED, we can plot false matches against the valid matches and we find that the best value
is to use about 200 elements to describe the--to describe each gram--characters.
That gives us an optimal separation between correct and invalid matches.
So that's, you know, that's the basic technique and those who came to the demonstration today
will have seen--this gives you--this gives you a really strong ability to uniquely identify
printed instances.
So what we would do in the experiments here is see how this was affected by these particular
substrates that the documents were printed on.
So we've got several examples here, we got laser printed examples, inkjet printed examples,
printed on a wide cross section of paper types.
And what we find is that right across it, I mean, you know, we found that we got good
results right across this set of paper types.
The only type of paper types that didn't work very well were actually these--these--the
handmade paper types which have a very--a lot of paper structure, which is a counter
intuitive result, because you would expect that the amount of, you know, that having
more random information would actually give you more information in your shape description,
and yet we found that--that wasn't the case.
And that's because these paper types have a systematic structure to them, and what this
method is relying on is the random element rather than systematic element.
Another way we can represent the difference between our populations of correcting and
correct matches is to--is to use a modified zed-score which gives the distance between
two populations.
If we look at results in this case, we find that apart from the two handmade paper types,
we're getting good results with zed-scores between five and six and above, and those,
you know, that means that you've got to like one in a billion chance of incorrectly--of
incorrectly getting a false positive or a false negative.
But rather, we also had a look, what happens if you only use the substrate part of the
information and ignore what you've printed.
So it's only using the substrate, and quite interestingly we're also--we're able--we aren't
using anything you print, just using the variations in the substrates underneath, you're still
able to differentiate correct and incorrect matches.
And the reason, you know, so provided you're using your character to give you an accurate
registration, we can still differentiate correct and incorrect matches.
Apart from the case of the really glossy paper down on the bottom right hand side because
it so glossy, it has no information content.
So, in conclusion, we've shown that you can get forensic level of authentication across
a wide variety of substrates.
And that substrate, only forensic authentication is possible for some of the subjects and for
most substrate types.
So this supports the use of prints as a forensic marker, it's ubiquitous and cheap, and it's
part of--you know, we print things and we can--we can, you know, it's amazing that you
can use print to give you this level of identification of documents.
So it's a simple solution and it's applicable to any text glyph or printed mark.
Thank you.
>> Are there any questions?
>> Have you tested this using different resolution cameras to sample the signature and to test
it at either end?
>> POLLARD: So we didn't test--well we tried it with scanners and scanners are too low
resolution to do it.
But one thing we have done--so this is an area by sense.
So, you would have to start--at the moment you would rely on if it was attached to a
printer, you would have to have the document stationary and either are used or would have
to offer, you know, to scan it, or we would have to go and be stored in a bin and sample.
So, what we've also looked at is using line scan sensors so that could be used as a document
which is passing through them--past them.
And that's--that's slightly low resolution, but it's still like, five microme pixels.
We don't think--we don't think you can get less than about five microme or ten microme.
>> I was just thinking that today we can get five megapixel cameras cheaply, next year
it will be eight megapixel...
>> POLLARD: Oh.
So you mean the other way?
>> Yes >> POLLARD: So, no.
We don't mind going--yes, you can--a higher resolution will not be an issue.
Maybe that you, you know, you have to sensor size--sensor size, typically it stays the
same.
It just gets chopped up into some more and more pieces.
There would be an issue of going, you know, already, you know, you need sufficient sensor
size to cover a large enough piece of printed--a large enough printed item, so a larger piece
of text, or any other printed mark.
But it doesn't matter, as long as you, you know, less than, let's say five micromes,
then I think things are okay.
>> Thank you.
We're running out of time so thank you very much to you.