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All right, now let's listen
to what Adam Howie has to
say. He's an artist living in Berlin and
he studied in New York City,
actually hands up in the audience who a
few back his Kickstarter campaign
on RF signal blocking phone
cases. And he.
OK, so
how are we going to talk about it is
going to talk about Rita
countersurveillance 15 most
unwanted and wanted
things for
surveillance and countersurveillance.
All right.
Thank you for everyone to coming
here, this is my first C.C.C.,
and in the title
of this presentation, what I wanted
to talk about originally
was 50 companies
doing retail surveillance
and come up with 50
ways of doing it, a
retail model for countersurveillance.
But what I found as I began
working on that presentation
is that it's relentless.
It's literally thousands
of companies and
nonstop battle to keep up
with all the different retail
surveillance tactics
and modify your life to
adjust your privacy settings accordingly.
So I've had I've changed
the presentation a little bit to
focus on one
one aspect of that.
So I've narrowed it down to
photography or
computer vision and
even within computer vision,
narrowed it down to facial
analysis.
And for me, this started
when I used to work as a photographer.
When I moved to New York City
about 12 years ago, I worked
as a photographer, but I came
across some quotes from
Susan Sontag.
And as I read her book on
photography, it really influenced
my perspective about the power,
the ability for the camera
to capture,
to possess, to turn
people into objects
that you can possess and control
the narrative for.
And I think that's very clear
when you look at aggressive
paparazzi behavior,
such as these photographers
kind of attacking Britney Spears.
But I think it's a little bit less clear
how that narrative unfolds over
time online
for photos that are posted
to the Internet, to social media
and so on.
And to talk about computer
vision, I want to introduce a little
bit of the history of computer vision,
but it's not entirely new.
Nineteen sixty three is the first
recorded instance that I've come across
from a recently declassified
CIA memo
that was a proposal for a simplified
face recognition machine.
Ultimately, in 1963,
the technology was not quite
where it needed to be to perform,
you know, robust, accurate
facial recognition system.
But over the next few decades,
and especially in the year 1969,
three Japanese researchers
made a lot of progress and were able to
detect the first human face
with computer vision.
I like to think of these
first human face detections, which
look a little bit like the head of
broccoli or a light bulb and
the first cave paintings for the first
time that a computer was able to
to understand in
a very primitive way what it was to
appear as a human.
Throughout the 70s and 80s,
computer vision made only moderate
gains.
And in the 1990s, this
program called Ferrett,
which was funded by, of course,
military Department of Defense,
it was a feasibility study to
determine if facial recognition could
play a significant role in
law enforcement and
in identifying enemy combatants at
a distance.
So this set into motion
what Paul really calls
the logistics of military perception.
And in 2001,
a breakthrough algorithm came out
called the Viola Jones algorithm.
So the goal, the Jones algorithm
was unique because
it was very efficient and
offered enough accuracy
that you could deal deal
with the tolerance of the
the kind of cost benefit analysis of
a very lightweight computer
vision system that could be put
on to embedded hardware at a
very low cost with a decent
frame rate.
So what that set into motion
is the ubiquity of computer vision,
face detection, appearing on all sorts of
different devices in 2001.
For that that really changed
the model for where you could put
computer vision.
You didn't need a giant computer to do
it. We needed a very lightweight,
small embedded system.
And of course, that brought a lot of
problems for privacy.
Now you have computers that can recognize
faces, you can extract the
faces, you can begin to do facial
analysis, and it doesn't cost
you a lot of money. You can do it on very
cheap, low cost hardware.
So as as open KVI
and this face detection
algorithm began to propagate throughout
culture.
Now, what what was appearing in
the two thousand eight
around that around that time?
There's a real push towards
computational photography, using
cameras almost without
a human in the loop to recognize
people and extract knowledge
about them.
The same way that Susan Sontag talked
about people using a
camera to extract knowledge
and possess people as an object.
Now, it's very clear, you know,
around 2005, 2010,
that this is going to be the future
narrative, that computers will be
extracting the narratives
and labeling us and tagging us.
So in 2010,
I worked on a project that's
all about modulating
your appearance to reduce
your confidence score to
computer vision algorithm.
The project is called CV Dazzle
and it uses the
vulnerabilities in the face detection
profiles and
exploits those with hair and makeup.
So by doing hair and makeup in
a certain location, you decrease
the confidence score that that phase
will appear.
What it looks like when you
run a test, probably.
Speed this up, I can.
You can watch in very
slow down version
how fast the action works, and
it's really just reading an image from
left to right like a book,
and you can see the results
of the algorithm on the left,
very high confidence score
for the face. Now, if we fast forward
to the end,
what we see is a zero
confidence score.
Well, there's one
misplaced rectangle compared
to a
very high confidence score on the left.
Another way to look at that is to use
what's called a salience map to
understand it's kind of like a heat map
for where computer vision algorithm is
looking. What it found interesting
and salient in that image.
So you can go back and
and see kind of where the computer
vision eyes have been looking at an image
recently, researcher
Voytek Frige in
University of West Bohemia
ran a study on
TV Dazzle to determine
how effective or whether it was effective
at all.
And what he found is not
that effective one hundred percent of the
time, which I don't think should be
a requirement for camouflage.
And I think camouflage is often
misunderstood as a Harry
Potter invisibility cloak, when
camouflage actually actually is about
optimizing the way that
you appear and reducing visibility,
moving matter between different
parts of the electromagnetic spectrum,
possibly just
even for a brief moment to evade
observation.
So achieving 100 percent,
of course, would be great, but I don't
think that should be a requirement for
the way that we think about camouflage.
The results of his
analysis show that the most effective
pattern was when you cover
the nose bridge area.
So that's one of the biggest
vulnerabilities of open sea, these face
detectors.
And the result was about 69
percent reduction in detection.
Now, if we compare that
to World War One, dazzle,
camouflage, the original devil.
This has been debated whether it was
effective at all or not.
But Roy Barens, a camouflage historian,
has said now, in fact,
Dalzell was evaluated and
it was about 50 percent effective
that he could say 50 percent is not great
for camouflage.
But if you avoid one out
of every two torpedoes, that
would explode your ship.
That's that's pretty great.
Since I worked on the project, it's kind
of taken on a life of its own,
appearing on TV show
Elementary
people have kind of taken the
hints from what I posted online,
reinterpreted in their own way.
Which sometimes turns out great,
sometimes turns out.
Very interesting.
But I'm happy,
but I'm happy to see overall that people
are experimenting with
this idea of just appearing
in a new way, and I think you can also be
very playful.
After that project, which is in 2010,
in 2013, became
very aware and concerned about a
different type of imaging, which is
thermal surveillance doesn't relate as
much to retail yet,
but thermal is becoming, you know,
very cheap.
Ten years ago, 640 by
480 would cost you twenty thousand
dollars, 320 by
240 today cost two hundred
dollars.
So the price has
changed the way that we use this
technology and thermal is becoming
more and more of a consumer level
technology.
So what do you see here as a way to
block? This is more of the Harry Potter
invisibility cloak kind of technology.
It's a silver plated
metal fabric fashioned
in Islamic dress as
a anti drone hijab
and an anti drone burka.
The idea of the burqa
is that it's reinterpreting
religious dress in an era
of mass surveillance to
instead of create a separation
between man and God,
create a separation between man
and drone.
You can see that.
Now, this is a test for you,
like that game you play for
Hunt.
There are four people and you can see
them very clearly, their heat signature.
But there's a fifth person wearing
the anti drone burqa.
May be hard to see because of the
projection I'll give you the
benefit of the doubt here, I'm going to
play the animation will become very
clear when there's motion in it, but
without motion.
I can see that the visibility is is
near zero for wearing the
anti drone burqa.
Therefore, it's very clear now you can
see the legs and that's intentional.
Now what you'll see is somebody walk out
of a store. This is in the winter.
There's actually quite a high temperature
differential.
And this person is just glowing.
These projects, I approach them
in a in a playful way,
but they also touch on some
very serious issues of national security
and surveillance.
And what you
know, I can't predict is who will
find these threatening or interesting
after releasing these earlier projects.
One of the people that found that
interesting was the Air Force
general counsel at the Pentagon
who treated the project.
And the other one, it's kind of funny,
a request for an internal use
only publication
from a three letter agency
asking for permission.
Well, I'm never going to see it.
I guess so, um,
so this kind of working in this
area, uh,
a lot of uncertainty about the way that
people would perceive these projects.
As for a comment,
the NSA declined,
obviously, to say anything about it.
But it always makes me wonder where
where the line is in doing this kind of
artwork. How far can you really go before
you've gone too far?
And I think you should just go
further. So I've taken the idea
of these garments and
created a kind of shop called the Privacy
Gift Shop, where I try to further
these ideas
and kind of I using the
store to normalize
through commerce.
I think Holly Herndon said it best in
a talk she gave using pop
culture as a carrier signal
to transmit these ideas.
And I think commerce and the gift shop
can have a friendly normalizing effect
on otherwise kind of terrifying
discourse around national security.
So originally, I did want to talk about
these topics. I've noticed some great
talks here that
I want to point you to,
to kind of.
Allow me to expand on on the
facial recognition computer vision,
wolfie crystals, talk about corporate
surveillance and another
interesting talk about the ultrasound
tracking ecosystem.
So these are kind of all part of the
corporate retail surveillance
infrastructure.
I'll just add two things to
that. With wi fi, you can now detect
motions with wireless signals.
And I think this is worth spending too
much time on. It's very easy to block
this with about a ten dollar
E.S.P, 80 to 66
wi fi. You can turn this into a kind of
jammer to create a lot of noise
in the Wi-Fi spectrum and disrupt the
motion detection signals.
Another company to highlight is Indore
Atlas, which uses the geomagnetic
signature measuring
the gas on your phone's digital
magnetometer to
get a to meet a resolution
position within an indoor retail
environment.
But that's also very easy to block
with a street magnet, put a sheet magnet,
put a
magnetic shielding material on your phone
case, or even a small piece of metal
will change that gas signature
enough to throw off.
So these are both very easy
technologies to circumvent.
What I want to talk about then, is
computer vision and kind of modulating
your appearance to minimize the damage
to privacy.
As I was preparing this, I came across
there as a designer, were
influenced by everything.
But what better element to pull from than
something that's evolved over time?
Birds have shorter wingspans now today
because they need to be more aerodynamic
to not be hit by cars.
That's something that's happened in the
last hundred years.
And I thought this is a great metaphor
for thinking through technology that we
also need to evolve like birds.
The problem is that's not really true.
So Lockheed Martin,
based that on
a 2013 study
about I think
Cliff swallows and it's
a bit misleading to
put it nicely, because
the study is very short sighted,
looking only at Nebraska
and the
birds lived
near near traffic in a bridge
structure.
So to make this,
you know, extrapolation from the data,
I think that's actually the metaphor
is that we're we're over interpreting
statistics to create a lot of
hype and oversell technologies
that mislead us about their true cost to
society.
So that's the metaphor.
And we can now we finally get to
more computer vision, part of it
to to put things
to put the scale and reference of
how much data can be
gleaned from a
very small amount of visual information.
We started the scale of one pixel, a one
by one transparent pixel.
And this is the most popular image in
the world in terms of the
number of times it's been downloaded
displayed.
Of course, you can't see it because it's
transparent.
And the only purpose of this image
is to collect information about you.
The pixel lives on Google dot com
lives all over.
The ad ecosphere can be represented
in 43 bytes.
So it's the most lightweight image.
I think this might be a better
metaphor for what images are today
in image. In some ways, it's become
a shell for data
collecting data and surveillance.
When we move up and fill in that square,
we have two hundred and fifty six
different values at grayscale,
that number
increases very quickly
as we increase the size of the
image to four
billion possible combinations and a two
by two grayscale image,
they go to four by four.
Now we're at three point four times ten
to the thirty eight.
We go to seven by six.
And now we have enough information to do
facial recognition.
Seven by six at
two hundred and fifty six.
Great values is enough to do
it. 95 percent accurate facial
recognition on the AT&T
faces data set that granted the
AT&T faces data set is
all white guys and it's not very
large data set.
Even with a larger
data set called Face Scrub, you
only get an 18 percent performance
reduction compared to the original
faces.
When you pixellated to 14
by 14 pixels
and only 12 by 16 pixels,
you can build an encoder to do
scene recognition activity recognition.
So what do you do in this case as you
train your neural network on this low
resolution data and then instead
of interpreting a 640
by 480 image scale,
that and you use the knowledge at
12 by 16 to interpret it.
We have a very large amount of
information in a very small image
space and we
go out to 20 by 20 here we have
two hundred and fifty six to the four
hundredths of very large dimensional
space. And these are
the next four images are the optimal
activations of open curves.
Ha Kaskade.
So if you were to ask
the algorithm
to describe the perfect
face, these would be
the most perfect face that the algorithm
would want to see activates
it maximally
very high confidence score.
So the different profiles are called alt
Ottery
just default frontal face.
Now we go up to one
hundred by 100.
What can we do at one hundred.
By one hundred I have a feeling, but
I run out of time talking about
everything that we can do.
One by one hundred two point five percent
of one Instagram photo,
as we've seen going up from one
pixel, even add one pixel.
There's a lot of information.
You have one hundred percent unique
severability from zero
to nine with times new Roman.
You have ninety seven percent unique
severability.
When you reduce each character to one
pixel, you know what that means is you
can take a lot of redacted
text, run it through a genetic
algorithm or Markov chain,
and then you can ascertain
what that text was in merely
a two by six pixilated image.
Pixelation is not really redaction.
It's simply a reduction and sometimes
that's enough information to tell
you what you want to know.
So
every day there are about three hundred
and seventy million photos uploaded
to the Facebook family,
Instagram and Facebook.
And what happens to all those photos
which contain a lot of faces and
a lot of those faces are larger than one
hundred pixels.
Every face that's uploaded to
Instagram or Facebook
is analyzed and
possessed and the knowledge is
extracted not only from the face,
but also the metadata
around that region.
Now, I do like a very quick
survey of some of the companies and
what they're doing with that data.
And I'll I'll focus on the most
pernicious ones, face ception,
there's a company out of Israel
that's looking at giving people,
ranking your image.
If you look like a terrorist,
if you look like a poker player, is one
of them bingo player,
you could be an academic researcher.
You can have a high IQ.
And they know all of this
by looking at your face.
So the idea is that your face
is somehow linked to your DNA
and that your physical traits
can describe who you are,
what you know, and your performance
as a human.
That sounds kind of crazy,
like Francis Galton eugenics,
inferring someone's
capabilities purely based on physical
traits know, but they're not alone.
So some of these scores I mentioned, high
IQ extrovert.
And another thing they can tell purely
by looking at your face is whether you
would be a good brand promoter.
Another research group is looking at
predicting criminality using
lip curvature, ie, inner
corner distance and nose mouth angle.
What they find is that criminal and
non-criminal face images populate
two quite distinctive manifolds.
Again, not allowing a lot of other
researchers are looking at how to take
that very small one hundred by one
hundred pixel amount of data
and turn it into insights
which could be used for marketing.
So here we have a
long list, including how trustworthy
you are, sociable, typical
or weird.
And what all this.
Reminds me of is Francis
Galton and eugenics and
and who the real criminal
in these cases would
be as people who are perpetrating this
idea, not the people who are
being looked at.
There are some interesting things to
learn from this, and I think by by
learning the ways that you're being
looked at, then you can modify
begin to game the system.
Of course, one of them is
if you have a wider mouth, you're more
likely to be chosen as leader
of CEO of a company.
But conversely, if you have a narrow
mouth, you're going to do better for
an NGO, says this paper.
You can just looking at
the relationship between these 100 by 100
pixel regions of face, determine who's
the most important person in an
image, spatial information.
You can ascertain somebody's pulse
if you have a video by amplifying
the green channel again.
Still within one hundred by one 100
pixels.
I don't think I have time to tell this
great story.
Jetpack, the company that analyzed
every public pixel of Instagram,
they then sold that technology
to Google.
Instagram is, of course, Facebook
brilliant if you sell
Facebook to Google.
But what they did is just take
information again from
the facial region and they built a
guidebook.
So what's a cool place to go?
Oh, the place with a lot of hipster
mustaches.
Where's a place to pick up girls
where photos of people with lipstick
are geotagged?
That's the idea for their product.
And that's what happens to photos that
are uploaded to Instagram.
But beyond that, you can also begin to
predict economic behavior
purely from one photo.
You can predict the decision making
capability about
20 percent better than a human can with
computer vision algorithms.
And I have to move briefly, but kind of
an example of what it would look like to
impose a lot of algorithms
on top of an image, a selfie,
I like to say, contains more information
than a mug shot because you not only have
the face, but you have all of the
metadata and relational
information around that.
One of the companies,
a few of them chiros
emotions
clarify Affectiva are operating
in this space using
some of the attributes I mentioned in
those earlier papers, as well
as this list of about
seventy eight
attributes that you can extract from a
face and 47
kind of knowledge points that you can
then infer based on those attributes.
So what to do with all this
information contained in a very
small two point five percent of one
Instagram photo?
Well, as I've looked at in an earlier
project, you can change
the way that you appear.
But I think there's also an opportunity
to change, you know, in camouflage.
You can think about the figure
and the ground relationship.
I think there's also an opportunity
to begin to modify
the ground, things that appear behind you
next to you, and that can possibly
interfere also with the computer vision.
Confidence score
in that new project is called hyper
phase. And what this is doing
is taking those maximal activations.
Either from a more traditional classifier
like the open KVI, the
all the drones, and here is a heat map
of the most important areas
of the face for two profiles,
taking that information and then just
giving an algorithm over,
overloading it with what it wants,
the kind of oversaturating an area
with faces to
increase, uh, to kind of
divert the gaze of a computer
vision algorithm,
know an early prototype for this
looked like something like this is a
little bit spooky. Maybe.
But what you see here is all the
maximal activation is kind of overlaid.
And, you know, when
you when you put this through a computer
vision phase detector,
you get about twelve hundred possible
phase detections from it.
Not faces but but confidence
mappings.
You can refine that a little bit to
create more of the kind of cad
pad pixellated camouflage look.
And then you can do something similar for
a neural network to activate the
face neuron of a neural network.
Then you can use these and combine
them to create for this project
textile textile patterns
that could be used, hopefully to
modify the environment around you,
whether somebody next to you,
whether you're wearing it, maybe around
your head or in a new way.
That project is in collaboration
with HyperMED Labs in New York City
for their new work, Neuro Speculative
Aphro, Feminism and
Asaph.
And that project
will come out in January.
So probably done.
But I like to end on this slide,
which was introduced to me, my friend
Richard Reeves, which shows
a scene from one hundred years ago in New
York. Now, if you look at this photo,
everybody's wearing a hat.
If you look around the room, nobody's
wearing a hat.
Right? So in one hundred years from now,
we're going to have a similar
transformation of fashion and the way
that we appear.
And what will that look like?
And hopefully it'll look like
something that's according to
appearing in a way where we we optimize
our personal privacy or
optimize.
Um, yeah,
optimize, you know, according to the
settings of mass surveillance.
And I'll end on that.
Thank you.