The Economist - USA (2019-08-17)

60 TheEconomistAugust 17th 2019

1

P

owered byadvances in artificial intelli-

gence (ai), face-recognition systems are

spreading like knotweed. Facebook, a so-

cial network, uses the technology to label

people in uploaded photographs. Modern

smartphones can be unlocked with it.

Some banks employ it to verify transac-

tions. Supermarkets watch for under-age

drinkers. Advertising billboards assess

consumers’ reactions to their contents.

America’s Department of Homeland Secu-

rity reckons face recognition will scruti-

nise 97% of outbound airline passengers

by 2023. Networks of face-recognition

cameras are part of the police state China

has built in Xinjiang, in the country’s far

west. And a number of British police forces

have tested the technology as a tool of mass

surveillance in trials designed to spot

criminals on the street.

A backlash, though, is brewing. The au-

thorities in several American cities, in-

cluding San Francisco and Oakland, have

forbidden agencies such as the police from

using the technology. In Britain, members

of parliament have called, so far without

success, for a ban on police tests. Refuse-

niks can also take matters into their own

hands by trying to hide their faces from the

cameras or, as has happened recently dur-

ing protests in Hong Kong, by pointing

hand-held lasers at cctvcameras. to dazzle

them (see picture). Meanwhile, a small but

growing group of privacy campaigners and

academics are looking at ways to subvert

the underlying technology directly.

Put your best face forward

Face recognition relies on machine learn-

ing, a subfield of aiin which computers

teach themselves to do tasks that their pro-

grammers are unable to explain to them ex-

plicitly. First, a system is trained on thou-

sands of examples of human faces. By

rewarding it when it correctly identifies a

face, and penalising it when it does not, it

can be taught to distinguish images that

contain faces from those that do not. Once

it has an idea what a face looks like, the sys-

tem can then begin to distinguish one face

from another. The specifics vary, depend-

ing on the algorithm, but usually involve a

mathematical representation of a number

of crucial anatomical points, such as the lo-

cation of the nose relative to other facial

features, or the distance between the eyes.

In laboratory tests, such systems can be

extremely accurate. One survey by the

nist, an America standards-setting body,

found that, between 2014 and 2018, the

ability of face-recognition software to

match an image of a known person with

the image of that person held in a database

improved from 96% to 99.8%. But because

the machines have taught themselves, the

visual systems they have come up with are

bespoke. Computer vision, in other words,

is nothing like the human sort. And that

can provide plenty of chinks in an algo-

rithm’s armour.

In 2010, for instance, as part of a thesis

for a master’s degree at New York Universi-

ty, an American researcher and artist

named Adam Harvey created “cv[comput-

er vision] Dazzle”, a style of make-up de-

signed to fool face recognisers. It uses

bright colours, high contrast, graded shad-

ing and asymmetric stylings to confound

an algorithm’s assumptions about what a

face looks like. To a human being, the result

Fooling Big Brother

Face off

As face-recognition technology spreads, so do ideas for subverting it

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