- [Narrator] Amnesty International
conducted a three borough census
of surveillance cameras in New York
and found more than 15,000 cameras in public spaces.
- This is just the part of the iceberg we can see.
- [Narrator] A 2021 survey found
that about 17% of American respondents
own smart security cameras.
Many were private doorbell cameras.
- [Albert] When you look at how many other brands of cameras
there are out there,
we're probably talking
about hundreds of thousands of cameras.
- These sensors have also become a lot more intelligent.
They're able to identify moving objects.
All of which makes the data much, much richer.
Your life can be rewound and your secrets can be revealed.
- [Narrator] "Wired" spoke to several experts
about the explosion of surveillance tech,
how police use it, and what the dangers of it might be.
[soft frenetic music]
- [Man] Immediate keyhole visual tasking.
- Target is on 20.
- [Narrator] The surveillance ecosystem
that could only be imagined
back in the 1998 film "Enemy of the State"
pretty much exists today,
complete with smart cameras
and high altitude aerial imaging.
- In the last two decades,
cameras have gotten a lot cheaper,
they've gotten smaller, they've gotten lighter
- A lot of the NYPD camera systems have 4K resolution,
night vision capabilities, 360 monitoring,
or have a swivel mount.
They could zoom in.
Some of them are even positioned
where they can see into the bedrooms of New Yorkers.
We see growing drone deployment across the country.
- [Arthur] The earliest predator cameras
came in this giant sensor ball.
That same sensing power
is now available on drone cameras
that weigh less than a kilogram.
- [Narrator] Arthur Holland Michel's book
argues that domestic law enforcement's adoption
of militarized drones and wide area surveillance
was born out of America's wars in the Middle East.
- [Arthur] War in the last two decades
has actually come to resemble policing
more than what traditionally was associated with conflict.
The technologies themselves
didn't need a lot of reworking or recalibrating
in order to have obvious use cases domestically.
- [Announcer] A single red kite unit
can continuously image an entire city size area
in medium resolution.
- [Narrator] This company's marketing video
shows an example of persistent Wide Area Motion Imagery,
or WAMI.
It allows for the tracking
of potentially hundreds of individuals simultaneously.
So once an incident is observed,
law enforcement can rewind
and track persons of interest in the area
all the way back to their homes.
- [Arthur] If you want to go back weeks in the footage
and see everywhere an individual has been,
you can do that because the camera is always recording
the entire frame.
- [Albert] If you map the route you take to school,
to work, to church, or to a mosque,
you can't get there unseen.
And in the age of facial recognition,
that means you can't get there untracked.
We know that the NYPD's use of facial recognition
is growing every year, and they're not an outlier.
That's true for police departments around the country
and it's now increasingly common
to see it used for graffiti, shoplifting,
other low level offenses.
- [Narrator] In fact, many major retailers
have used or currently used image recognition cameras
to monitor people in their stores.
So, how does it work?
- Facial recognition works in very specific situations.
For example, when we enter the airport in a passport control
and it uses face rec to check our face against our passport
'cause it has to check one image against another image.
We call this a one-to-one comparison.
On a public camera, for example,
if you search for a specific person, for example, a robber,
you're not doing a one-to-one comparison anymore
but you do a one to possibly thousands of faces,
and there it becomes less reliable.
- [Albert] The problem is that facial recognition
depends a lot on the quality of the photo.
If you're looking at someone straight on,
if you have them well lit,
if you have a high resolution image,
it can be pretty accurate.
But that's not how most camera footage comes in
from crime scenes.
You get blurry images taken at night,
low resolution from an off angle,
just seeing the side of someone's face.
- [Narrator] Facial recognition
is only as good as the data recorded
and the database it's being compared against.
The first type of database used in facial recognition
are the ones maintained by law enforcement.
- [Arthur] There are also databases
generated by Departments of Transportation, or DMVs,
that collect people's passport photo
or driver's license photo.
There have been cases
where the law enforcement agency has grainy CCTV image
of an individual who they're looking for
and they may pass that to the DMV
and run a facial recognition search.
- [Narrator] And the third category of databases
is potentially the largest of all: social media.
- We see facial recognition firms like Clearview AI
going onto social media sites like Facebook,
and Twitter, and Instagram
and just scraping data, taking our images in bulk,
downloading them, ingesting them into their database.
Chances are that whoever you are, watching this right now,
your image is in a Clearview AI database
and there's nothing you can do about it.
- [Narrator] Police are increasingly turning
to so-called fusion systems to help them connect the dots.
Streamlining what once took countless hours
of pavement pounding, video watching,
and digging through various databases.
- [Florian] And the more data there is out there,
it becomes more and more complicated for our end users
to really find information that they're looking for.
- [Narrator] Florian Matusek works for Genetec in Vienna.
They make Citigraf,
a fusion software system
that pulls in various threads of data for law enforcement
under a central display window.
- You could tell Citigraf,
"I want to see every time there was a theft reported
in a radius of 500 meters."
This gives you a very good filtering
to find more relevant information, more relevant events
by combining different data sources
and show you this information on the map.
- [Narrator] Another big player
in the intelligence fusion space is Microsoft
which helped expand the New York Police Department's
in-house machine learning fusion system,
initially developed to prevent terrorism after 9/11.
- [Arthur] The NYPD uses a product
called the Domain Awareness System,
effectively a fusion tool
which consolidates all of the NYPD's data
that may be used in investigations in a single program
in a single app that police officers
can even have on their smartphones;
footage from the city's thousands of CCTV cameras,
license plate readers, criminal record information.
If a report of a crime comes through,
they can open the software,
pull up license plate reader
to see what vehicles were nearby.
- [Narrator] Not only is the data getting organized
and the cameras getting smarter,
they're all connected to the internet now.
- Now that may seem like not such a big deal,
but once a CCTV camera is connected to the internet,
that information can be shared widely and instantaneously.
That makes these enormous repositories of data available
to really anybody who has the access code.
- [Narrator] In their ongoing cataloging
of surveillance cameras throughout New York city,
Albert Fox Cahn's group, STOP,
discovered a potential security vulnerability.
- By searching the internet
for the IP addresses of these cameras,
what we found was one company, Hikvision,
that had left its cameras unshielded.
Most companies hide the location of their devices,
but Hikvision didn't.
We found more than 16,000 Hikvision cameras
in New York city.
- Hikvision is a Chinese manufacturer
of internet enabled surveillance cameras.
It's controlling shares are owned by the Chinese government.
That's why some software manufacturers, like Genetec,
avoid using Hikvision sensors for their systems.
- Certain Chinese camera manufacturers
do not take type of security so seriously as they should.
Any time that you digitize and connect surveillance data,
you create an attack surface
from actors both within and beyond.
- [Narrator] So how do we watch the Watchers?
How do we make sure
that the images going from camera to cloud are protected?
One line of defense is video encryption software.
- In real time, it pixelates all movement in the image.
So you can imagine
if there is an operator sitting in front of the screens
watching the videos,
the operator does not know who he or she's watching.
If really something happened and evidence is needed,
then it's possible to go back into the recording
and based on user privileges to access the original video.
- [Narrator] But even encrypted video
can be unlocked by law enforcement,
if they have a warrant.
- Sometimes the government doesn't even need a warrant.
We see police getting a direct link to these camera systems.
So Amazon Ring's partnership with police,
thousands of agreements
with the different police departments,
setting up law enforcement portals
where it's easier for officers to identify the cameras
in an area and to send requests for footage.
It's your choice.
But the truth is people don't feel free to refuse.
And even if you're not willing to hand it over,
if it exists on Amazon servers or Google servers,
they can hand over your footage, whether you like it or not.
- We all certainly want to reduce violent crime,
especially when it appears to be on the upward trend,
but we have to be very conscious of the costs
of having these technologies.
If you live in a city
and you see that the mayor is using robotic systems
or drones or artificial intelligence to watch the city,
you are gonna think twice perhaps about going to a protest.
You're going to think twice about what you say or do.
- [Albert] In the aftermath of George Floyd's murder,
we saw customs and border protection
working with state and local police departments
to deploy predator style drones to surveil protests.
- [Narrator] Perhaps the most high profile case
has been that of Derrick Ingram,
who police accused of using a bullhorn
too close to an officer's ear.
Police used a photo taken of him at a rally
and identified him by matching it to social media.
- [Albert] They stormed his block,
sent heavily armed officers
to surround his building, to intimidate him,
to retaliate against him because of his activism
all while holding a facial recognition printout
in their hand.
When we talk about this technology,
it's not about some abstract violation of our rights.
- [Narrator] Further automation
through so-called predictive policing systems
have their own pitfalls.
- When you look underneath the algorithmic hood
we see a lot of pseudoscience and Silicon Valley snake oil.
- And the more we collect and the more we combine,
the more there is a danger
that we, as humans, trust technology too much
and think that it is perfect.
So this is why we should never go in a direction
where this information is combined automatically
and decisions are being made automatically based on it.
- Part of what I find so dangerous here
isn't just that we have more cameras,
isn't just the way that they could be best used by police,
but the fact that we have this growing culture
of surveillance driven fear
where people are constantly retreating behind their cameras
worried more and more about their neighbors
trained by these platforms to view them as a threat.