  We showcase a family of common failures of state-of-the art object detectors.
These are obtained by replacing image sub-regions by another sub-image that
contains a trained object. We call this "object transplanting". Modifying an
image in this manner is shown to have a non-local impact on object detection.
Slight changes in object position can affect its identity according to an
object detector as well as that of other objects in the image. We provide some
analysis and suggest possible reasons for the reported phenomena.
