They work because machine vision and human vision are different.
Powered
by advances in artificial intelligence (ai), face-recognition systems are
spreading like knotweed. Facebook, a social network, uses the technology to
label people in uploaded photographs. Modern smartphones can be unlocked with
it. Some banks employ it to verify transactions. Supermarkets watch for
under-age drinkers. Advertising billboards assess consumers’ reactions to their
contents. America’s Department of Homeland Security reckons face recognition
will scrutinize 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 authorities in several American cities,
including 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. Refuseniks can also take
matters into their own hands by trying to hide their faces from the cameras or,
as has happened recently during protests in Hong Kong, by pointing hand-held
lasers at cctv cameras. 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 learning, a subfield of ai in which computers
teach themselves to do tasks that their programmers are unable to explain to
them explicitly. First, a system is trained on thousands of examples of human
faces. By rewarding it when it correctly identifies a face, and penalizing 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 system
can then begin to distinguish one face from another. The specifics vary,
depending on the algorithm, but usually involve a mathematical representation
of a number of crucial anatomical points, such as the location 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 algorithm’s armour.
In 2010,
for instance, as part of a thesis for a master’s degree at New York University,
an American researcher and artist named Adam Harvey created “cv [computer
vision] Dazzle”, a style of make-up designed to fool face recognizers. It uses
bright colours, high contrast, graded shading and asymmetric stylings to
confound an algorithm’s assumptions about what a face looks like. To a human
being, the result is still clearly a face. But a computer—or, at least, the
specific algorithm Mr Harvey was aiming at—is baffled.
Dramatic
make-up is likely to attract more attention from other people than it deflects
from machines. HyperFace is a newer project of Mr Harvey’s. Where cv Dazzle
aims to alter faces, HyperFace aims to hide them among dozens of fakes. It uses
blocky, semi-abstract and comparatively innocent-looking patterns that are
designed to appeal as strongly as possible to face classifiers. The idea is to
disguise the real thing among a sea of false positives. Clothes with the
pattern, which features lines and sets of dark spots vaguely reminiscent of
mouths and pairs of eyes (see photograph), are already available.
An even
subtler idea was proposed by researchers at the Chinese University of Hong
Kong, Indiana University Bloomington, and Alibaba, a big Chinese
information-technology firm, in a paper published in 2018. It is a baseball cap
fitted with tiny light-emitting diodes that project infra-red dots onto the
wearer’s face. Many of the cameras used in face-recognition systems are
sensitive to parts of the infra-red spectrum. Since human eyes are not,
infra-red light is ideal for covert trickery.
In tests
against FaceNet, a face-recognition system developed by Google, the researchers
found that the right amount of infra-red illumination could reliably prevent a
computer from recognizing that it was looking at a face at all. More sophisticated
attacks were possible, too. By searching for faces which were mathematically
similar to that of one of their colleagues, and applying fine control to the
diodes, the researchers persuaded FaceNet, on 70% of attempts, that the
colleague in question was actually someone else entirely.
Training
one algorithm to fool another is known as adversarial machine learning. It is a
productive approach, creating images that are misleading to a computer’s vision
while looking meaningless to a human being’s. One paper, published in 2016 by
researchers from Carnegie Mellon University, in Pittsburgh, and the University
of North Carolina, showed how innocuous-looking abstract patterns, printed on
paper and stuck onto the frame of a pair of glasses, could often convince a
computer-vision system that a male ai researcher was in fact Milla Jovovich, an
American actress.
In a
similar paper, presented at a computer-vision conference in July, a group of
researchers at the Catholic University of Leuven, in Belgium, fooled person-recognition
systems rather than face-recognition ones. They described an algorithmically
generated pattern that was 40cm square. In tests, merely holding up a piece of
cardboard with this pattern on it was enough to make an individual—who would be
eminently visible to a human security guard—vanish from the sight of a computerized
watchman.
As the
researchers themselves admit, all these systems have constraints. In
particular, most work only against specific recognition algorithms, limiting
their deployability. Happily, says Mr Harvey, although face recognition is
spreading, it is not yet ubiquitous—or perfect. A study by researchers at the
University of Essex, published in July, found that although one police trial in
London flagged up 42 potential matches, only eight proved accurate. Even in
China, says Mr Harvey, only a fraction of cctv cameras collect pictures sharp
enough for face recognition to work. Low-tech approaches can help, too. “Even
small things like wearing turtlenecks, wearing sunglasses, looking at your
phone [and therefore not at the cameras]—together these have some protective
effect”. ■
***This
article appeared in the Science and technology section of the print edition
under the headline "Face off"
fecha |
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