As face-recognition technology spreads, so do ideas for subverting it – The Economist August 2019

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 scrutinise 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 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 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 recognisers. 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.

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