Deep artificial neural networks have been proposed as a model of primate vision. However, these networks are vulnerable to adversarial attacks, whereby introducing minimal noise can fool networks into misclassifying images. Primate vision is thought to be robust to such adversarial images. We evaluated this assumption by designing adversarial images to fool primate vision. To do so, we first trained a model to predict responses of face-selective neurons in macaque inferior temporal cortex. Next, we modified images, such as human faces, to match their model-predicted neuronal responses to a target category, such as monkey faces. These adversarial images elicited neuronal responses similar to the target category. Remarkably, the same images fooled monkeys and humans at the behavioral level. These results challenge fundamental assumptions about the similarity between computer and primate vision and show that a model of neuronal activity can selectively direct primate visual behavior.
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