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Driverless Cars Could Be Hacked With Stickers on Traffic Signs, Study Suggests

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Justin Sullivan/Getty Image

As driverless cars inch toward becoming regular sights on our streets, experts have started to warn that the connected cars could be vulnerable to hackers who can take control of the vehicles from a distance. Though most of these warnings are related to hacking into the internet-connected computer on board, there’s an analog way to disrupt the workings of a driverless car, too, as Autoblog reports. Researchers from across the U.S. recently figured out how to trick a driverless car with a set of stickers, as they detail in a paper posted on arXiv.org.

They examined how fiddling with the appearance of stop signs could redirect a driverless car, tricking its sensors and cameras into thinking that a stop sign is actually a speed limit sign for a 45 mile-per-hour zone, for instance.

They found that by creating a mask to cover the sign that looks almost identical to the sign itself (so a human wouldn’t necessarily notice the difference), they could fool a road-sign classifier like those used by driverless cars into misreading the sign 100 percent of the time.

Five different views of a stop sign with black and white block-shaped stickers seen from various angles and distances.

Evtimov et al., arXiv.org

In a test of a right-turn sign, a mask that filled in the arrow on the sign resulted in a 100 percent misclassification rate. In two thirds of the trials, the right-turn was misclassified as a stop sign, and in one third, it was misclassified as an added lane sign. Graffiti-like stickers that read “love” and “hate” confused the classifier into reading a stop sign as a speed limit sign the majority of the time, as did an abstract design where just a few block-shaped stickers were placed over the sign.

“We hypothesize that given the similar appearance of warning signs, small perturbations are sufficient to confuse the classifier,” they write.

The study suggests that hackers wouldn’t need much equipment to wreak havoc on a driverless car. If they knew the algorithm of the car’s visual system, they would just need a printer or some stickers to fool the car.

However, the attacks could be foiled if the cars have fail-safes like multiple sensors and take context (like whether the car is driving in a city or on a highway) into account while reading signs, as Autoblog notes.

[h/t Autoblog]

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11-Year-Old Creates a Better Way to Test for Lead in Water
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In the wake of the water crisis in Flint, Michigan, a Colorado middle schooler has invented a better way to test lead levels in water, as The Cut reports.

Gitanjali Rao, an 11-year-old seventh grader in Lone Tree, Colorado just won the 2017 Discovery Education 3M Young Scientist Challenge, taking home $25,000 for the water-quality testing device she invented, called Tethys.

Rao was inspired to create the device after watching Flint's water crisis unfold over the last few years. In 2014, after the city of Flint cut costs by switching water sources used for its tap water and failed to treat it properly, lead levels in the city's water skyrocketed. By 2015, researchers testing the water found that 40 percent of homes in the city had elevated lead levels in their water, and recommended the state declare Flint's water unsafe for drinking or cooking. In December of that year, the city declared a state of emergency. Researchers have found that the lead-poisoned water resulted in a "horrifyingly large" impact on fetal death rates as well as leading to a Legionnaires' disease outbreak that killed 12 people.

A close-up of the Tethys device

Rao's parents are engineers, and she watched them as they tried to test the lead in their own house, experiencing firsthand how complicated it could be. She spotted news of a cutting-edge technology for detecting hazardous substances on MIT's engineering department website (which she checks regularly just to see "if there's anything new," as ABC News reports) then set to work creating Tethys. The device works with carbon nanotube sensors to detect lead levels faster than other current techniques, sending the results to a smartphone app.

As one of 10 finalists for the Young Scientist Challenge, Rao spent the summer working with a 3M scientist to refine her device, then presented the prototype to a panel of judges from 3M and schools across the country.

The contamination crisis in Flint is still ongoing, and Rao's invention could have a significant impact. In March 2017, Flint officials cautioned that it could be as long as two more years until the city's tap water will be safe enough to drink without filtering. The state of Michigan now plans to replace water pipes leading to 18,000 households by 2020. Until then, residents using water filters could use a device like Tethys to make sure the water they're drinking is safe. Rao plans to put most of the $25,000 prize money back into her project with the hopes of making the device commercially available.

[h/t The Cut]

All images by Andy King, courtesy of the Discovery Education 3M Young Scientist Challenge.

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Google's AI Can Make Its Own AI Now
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Artificial intelligence is advanced enough to do some pretty complicated things: read lips, mimic sounds, analyze photographs of food, and even design beer. Unfortunately, even people who have plenty of coding knowledge might not know how to create the kind of algorithm that can perform these tasks. Google wants to bring the ability to harness artificial intelligence to more people, though, and according to WIRED, it's doing that by teaching machine-learning software to make more machine-learning software.

The project is called AutoML, and it's designed to come up with better machine-learning software than humans can. As algorithms become more important in scientific research, healthcare, and other fields outside the direct scope of robotics and math, the number of people who could benefit from using AI has outstripped the number of people who actually know how to set up a useful machine-learning program. Though computers can do a lot, according to Google, human experts are still needed to do things like preprocess the data, set parameters, and analyze the results. These are tasks that even developers may not have experience in.

The idea behind AutoML is that people who aren't hyper-specialists in the machine-learning field will be able to use AutoML to create their own machine-learning algorithms, without having to do as much legwork. It can also limit the amount of menial labor developers have to do, since the software can do the work of training the resulting neural networks, which often involves a lot of trial and error, as WIRED writes.

Aside from giving robots the ability to turn around and make new robots—somewhere, a novelist is plotting out a dystopian sci-fi story around that idea—it could make machine learning more accessible for people who don't work at Google, too. Companies and academic researchers are already trying to deploy AI to calculate calories based on food photos, find the best way to teach kids, and identify health risks in medical patients. Making it easier to create sophisticated machine-learning programs could lead to even more uses.

[h/t WIRED]

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