reported in the literature to affect DNNs [@Engstrom_etal_2017_NIPSW; @azulay2018deep]. Our results demonstrate that fragile recognition is independent from this phenomenon: bigger pooling regions reduce most of the lack of invariance to changes in object location, while pooling only marginally reduces fragile recognition. We also show that known strategies to increase network generalization,  [*i*.*e*. ]{}adding regularization and data augmentation, reduce the number of FRIs but still leave far more than humans have. These results highlight how much more fragile current DNN recognition ability is than human vision.

![*The process of generating a fragile recognition image (FRI) map from a full image.* The FRI map shows FRIs for which a one-pixel shift in any direction of the visible region produces an incorrect classification ([*i*.*e*. ]{}loose shift - see definition in \[sec:methods\]). The white pixels indicate FRIs and black pixels indicate non FRIs.[]{data-label="fig:MapGeneration"}](map-generation-diagram-1.jpg){width="14cm"}

Methods: Extracting Fragile Recognition Images {#sec:methods}
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In this Section we introduce the method for extracting FRIs for DNNs. The method for extracting human minimal images employs a tree search strategy [@Ullman_etal_2016_PNAS]. The full object is presented to human subjects and they are asked to recognize it. If at least $50\%$ of subjects recognize it correctly, smaller crops of the 