in which only a part of an object is observed, and a slight adjustment of the visible area produces a sharp drop in human recognition accuracy. Figure \[fig:HumanMinImgExample\] provides examples of human minimal images.

[@Ullman_etal_2016_PNAS] show that DNNs are unable to recognize human minimal images, and the DNN drop in accuracy for these minimal images is gradual rather than sharp. This begs the question of whether the sharp drop in accuracy for minimal images is a phenomenon exclusive to human vision, or there exist distinct but analogous images that produce a sharp drop in DNN accuracy.

![*Qualitative examples of human and DNN minimal images.* Human minimal images and their less-recognizable sub-images from [@Ullman_etal_2016_PNAS]; and DNN analogs for the same images.[]{data-label="fig:HumanMinImgExample"}](human-dnn-min-img.pdf){width="12cm"}

![*Qualitative examples of fragile recognition images (FRI).* Examples are for ResNet [@He_etal_2016] analogs to human minimal images. The incorrectly classified image region is slightly (two pixels) smaller than the correctly classified crop. Even when DNNs show a significant change in confidence between regions, humans recognize them at similar rates.[]{data-label="fig:DNNMinImgExample"}](min-img-examples-1.jpg){width="14cm"}

In this paper, we provide evidence for the latter hypothesis by showing that there is a large set of analogs to minimal images that affect DNNs. These are different from the 