Researchers at the University of Freiberg and the Bosch Center for Artificial Intelligence in Germany have demonstrated it is possible to prevent machine-vision systems from seeing specific categories of objects in a scene, such as pedestrians in the road.
The method works by strategically flooding an image with noise that degrades the artificial intelligence's (AI) ability to recognize objects, but keeps the image looking unaltered to humans.
The researchers say these "universal perturbations" are generated by an algorithm, and work regardless of what type of image, scene, or computer-vision system to which they are being applied.
Instead of stopping the algorithm from identifying the entire image, the algorithm uses a process called "semantic segmentation," which divides the image into groups of pixels in order to identify different types of objects in the scene, enabling the researchers to prevent the AI from perceiving specific objects in the scene.
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