the original image in a larger image and shift it in the image plane (while filling in the rest of the image with a simple inpainting procedure)”. The embedding of the image can also be done with a black empty background as in [@Engstrom_etal_2017_NIPSW], or using videos in which the background is static and only the object is moving [@azulay2018deep]. In fragile recognition, both the object and the background, [*i*.*e*. ]{}the entire image, change due to the shift or shrink of the image region. In previous works, only the object changes location. We introduce an experiment that reveals that this subtle difference makes fragile recognition an independent and more complex phenomenon than lack of location invariance.

[**Pooling induces location invariance.**]{} DNN architectures with large pooling regions are known to induce invariance to the object location, [*cf*. ]{}[@Anselmi_etal_2015]. A pooling layer operates independently for each feature by extracting the maximum activation across the spatial dimensions. For a pooling region of the same size as the full image, the network response is invariant to the object position within the image frame. This is under the assumption that there is enough spatial resolution in the responses in order to guarantee that the responses 