Skip to Content
Artificial intelligence

Computer Vision Algorithms Are Still Way Too Easy to Trick

December 20, 2017

AI image recognition has made some stunning advances, but as new research shows, the systems can still be tripped up by examples that would never fool a person.

Labsix, a group of MIT students who recently tricked an image classifier developed by Google to think a 3-D-printed turtle was a rifle, released a paper on Wednesday that details a different technique that could fool systems even faster. This time, however, they managed to trick a “black box,” where they only had partial information on how the system was making decisions.

The team’s new algorithm starts with an image it wants to use to trick another system—in the example from their paper, it’s a dog—and then starts altering pixels to make the image look more like the source image; in this case, skiers. As it works, the “adversarial” algorithm challenges the image recognition system with versions of the picture that quickly move into territory any human would recognize as skiers (check out the gif, above). But all the while, the algorithm maintains just the right combination of sabotaged pixels to make the system think it’s looking at a dog.

The researchers tested their method on Google’s Cloud Vision API—a good test case in part because Google has not published anything about how the computer vision software works, or even all the labels the system uses to classify images. The team says that they’ve only tried foiling Google’s system so far, but that their technique should work on other image recognition systems as well.

There are plenty of researchers working on countering adversarial examples like this, but for safety-critical uses, such as autonomous vehicles, artificial intelligence won’t be trusted until adversarial attacks are impossible, or at least much more difficult, to pull off.

Deep Dive

Artificial intelligence

Sam Altman says helpful agents are poised to become AI’s killer function

Open AI’s CEO says we won’t need new hardware or lots more training data to get there.

Is robotics about to have its own ChatGPT moment?

Researchers are using generative AI and other techniques to teach robots new skills—including tasks they could perform in homes.

What’s next for generative video

OpenAI's Sora has raised the bar for AI moviemaking. Here are four things to bear in mind as we wrap our heads around what's coming.

An AI startup made a hyperrealistic deepfake of me that’s so good it’s scary

Synthesia's new technology is impressive but raises big questions about a world where we increasingly can’t tell what’s real.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.