  The elementary operation of cropping underpins nearly every computer vision
system, ranging from data augmentation and translation invariance to
computational photography and representation learning. This paper investigates
the subtle traces introduced by this operation. For example, despite
refinements to camera optics, lenses will leave behind certain clues, notably
chromatic aberration and vignetting. Photographers also leave behind other
clues relating to image aesthetics and scene composition. We study how to
detect these traces, and investigate the impact that cropping has on the image
distribution. While our aim is to dissect the fundamental impact of spatial
crops, there are also a number of practical implications to our work, such as
revealing faulty photojournalism and equipping neural network researchers with
a better understanding of shortcut learning. Code is available at
https://github.com/basilevh/dissecting-image-crops.
