[ExI] Terrorist? Who can tell?
Harvey Newstrom
mail at HarveyNewstrom.com
Sun Aug 24 13:05:30 UTC 2008
"Lee Corbin" <lcorbin at rawbw.com> wrote,
> Harvey writes (forgive me for chopping up your email and replying to
> various parts, perhaps out of order)
That's what e-mail is for. :-)
> Well, to keep the issues straight, you are suggesting that
> if a known criminal X (of whatever race) passes through
> an airport that today's present face-recognition programs
> are pretty much useless?
Yes. Because of the statistics I exemplified, the system will not only spot
the known criminal, but a large number of people who are not wanted. They
will waste their time with 10,000 unwanted people for every watned one.
Over the course of a year, that means they will detain 27 innocent people
every day for a year before they get one wanted guy. The system won't last
that long. They will yank it and declare it doesn't work.
I'm not just theorizing this. We tried to use such a system in Florida at
our Bowl game a few years ago to catch wanted criminals and deadbeat dads
(not paying child support). Bowl security was swamped with hundreds of
false positives. They did not find a single wanted person, even though
there probably were some in the crowd. They then theorized that pre-bowl
publicity kept criminals from going to the game. So they deployed this in
Tampa's Ybor district and used it for a whole year. Not one real
recognition was made. All they got were false positives.
Google for "florida super bowl face recognition"
<http://www.google.com/search?q=florida+super+bowl+face+recognition>
> (Whereas human spotters, I presume, are not at all useless.)
I don't have evidence for this, but anectodally, humans were not much
better. The system matched exact facial dimensions, but got gender, height,
weight, race, and other obvious traits wrong. But if the criminal had
altered their appearance, with beard, hair colr, different hair-cut,
glasses, etc., the computer would still match it but the human double-check
would likely think it was a poor match. In general, I don't think facial
recognition (human or computer) works that well. Detecting fingerprints,
DNA, electronic IDs, vehicle registrations, RFID tags, and other exact
matches would probably work better. As well as actually trying to track
down a suspect to their actual location rather than randomly looking around
and expecting them to walk by. Again, it's all statistics. The number of
non-matches just overwhelms the (human and machine) systems.
> Or, contrariwise, do you mean terrorist-spotting software,
> e.g. which for argument's sake say are quite effective at
> distinguishing Middle-Eastern young men from Indian young
> men, are useless because of these statistical facts you adduce?
Yes. Such software is notoriously poor at recognizing race, which cannot be
precisely defined between groups. Even a small 1% overlap between racial
groups can cause the statistical large number of false positives. I can't
find the rigorous studies I have seen, but I found a couple of examples new
to me that describe the difficulties.
http://www.anonequity.org/weblog/archives/2007/06/are_biometrics_raceneutral.php
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1770976
> And (on the same point as this last paragraph) terrorist-spotting
> humans at, say the Tel Aviv airport---using every clue they can
> ---get way too many false positives to be of any use?
Tel Aviv does not rely on racial profiling or other profiling like the US is
trying to do. (They may use it, but it isn't key.) They screen every
single bag. They screen every single passenger. They lock passengers out
of the cock-pit. They have strong security unstead of underpaid TSA
contractors. And most importantly, they profile behaviors or dangerous
materials they want to detect, and not secondary characteristics like race
or religion that a very large number of innocent people have. Thus, they
don't get this massive list of false positives.
Tel Aviv has the strategy of keeping dangerous materials off planes by
looking for those materials, and keeping suspicious people off planes by
detecting suspicious behavior. The U.S. strategy seems to be trying to tell
who is guilty by their past credit histories and what they look like, while
doing a poor job searching their bags or watching their current/actual
behavior. The former strategy is more direct to the goal and more
objectively measurable than the U.S. strategy.
> Also, let me weaken that entirely separate claim a little bit to two
> questions, A and B:
>
> A: "Are telling me that a row of six or more recent convicted
> terrorist bombers could not be distinguished at the ninety-percent
> level of confidence from a numerically similar row of Londoners
> picked at random" Surely you agree that I'm right about *that*,
No, I don't agree. As I showed with my statistical correlation, that
false-positives overwhelm the systems a thousand times over. How would you
distinguish them? Are you saying they look different? Are you saying they
act different? Do you suppose there are forensics traces of dangerous
materials in their clothes? What do you claim to be looking for that could
be distinguished? Race isn't enough, because there are a lot of
middle-easterners in London. Religion, language, even hatred toward other
groups is not a good predictor of terrorism. Could you be more specific
about what exactly you think you could see? There is a reason we have
trials with evidence and just don't convict people because they "look"
guilty.
> B: "are you telling me that a row of six or more recent convicted
> terrorist bombers could not be distinguished at the ninety-percent
> level of confidence from a numerically similar row of Londoners
> of completely matching age and sex?"
I explained how a 9.9999% accurate system with only 0.0001% error rate
still isn't good enough. Your 99% or 90% aren't even in the ballpark. I
know it sounds counter-intuitive (as with much of Baysean statistics). But
if you work the numbers out of your random sampling, you simply won't get
correct answers more than you get wrong answers. The numbers just don't
work out most of the time.
(Being in the security field, I really am interested in new ideas and
schemes that might work. But many of the "obvious" schemes have already
been tried and failed.)
--
Harvey Newstrom <www.HarveyNewstrom.com>
CISSP CISA CISM CIFI GSEC IAM ISSAP ISSMP ISSPCS IBMCP
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