object, called descendants, are tested. If any of the smaller region is recognizable, the crop size is further reduced and tested. Once an image crop is found such that it is recognizable and all of its descendants are not recognizable, it is considered a human minimal image.

Our FRI extraction method for DNNs relies on an exhaustive grid search. This is possible in DNNs due to parallelization across multiple GPUs, and would be prohibitively time-consuming to replicate with humans. The grid search consists on a two-step process: first, every possible square region is classified by the DNN and the correctness is annotated in the [*correctness map*]{}. From the correctness map, each region’s correctness is compared with the region’s slightly changed location or size in order to determine if there has been a change of the correctness. FRIs are regions that are classified correctly and a small change causes failure, as well as regions that are classified incorrectly and a small change causes success.

The two step process to detect FRIs is summarized in Figure \[fig:MapGeneration\]. We now detail the two steps:

[**1. Correctness map generation.**]{} An exhaustive grid search is performed to see if each possible square image region of 