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

Yann LeCun
Yann LeCun

Yann LeCun has a bold new vision for the future of AI

One of the godfathers of deep learning pulls together old ideas to sketch out a fresh path for AI, but raises as many questions as he answers.

images created by Google Imagen
images created by Google Imagen

The dark secret behind those cute AI-generated animal images

Google Brain has revealed its own image-making AI, called Imagen. But don't expect to see anything that isn't wholesome.

AGI is just chatter for now concept
AGI is just chatter for now concept

The hype around DeepMind’s new AI model misses what’s actually cool about it

Some worry that the chatter about these tools is doing the whole field a disservice.

AI and robotics concept
AI and robotics concept

AI’s progress isn’t the same as creating human intelligence in machines

Honorees from this year's 35 Innovators list are employing AI to find new molecules, fold proteins, and analyze massive amounts of medical data.

Stay connected

Illustration by Rose WongIllustration 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 with a list of newsletters you’d like to receive.