Whether they’re detecting human faces in Snapchat or helping self-driving cars avoid road hazards, artificial intelligence systems depend on computer vision algorithms to distinguish between different types of objects. But researchers have developed tricks to confuse those algorithms, stopping AI from recognizing the contents of images.

A new method developed by German scientists from the University of Freiberg and the Bosch Center for Artificial Intelligence goes further, showing it’s possible to effectively blind machine vision systems from seeing specific categories of objects in a scene, like pedestrians in a road.

Like other recent studies, the trick works by strategically flooding an image with noise that degrades the AI’s ability to recognize objects, but keeps the image perfectly normal-looking to humans. These “universal perturbations” are generated by an algorithm, and work regardless of what type of image, scene or computer vision system they’re applied to.

Rather than stop the algorithm from identifying the entire image, however, the process targets a process called “semantic segmentation,” which chops up the image into groups of pixels in order to identify different types of objects in the scene. This allows researchers to block out certain things from the scene as the AI perceives it while leaving the rest of the scene intact, making the disturbance much harder for the system to detect.

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