  Many tasks in computer vision are often calibrated and evaluated relative to
human perception. In this paper, we propose to directly approximate the
perceptual function performed by human observers completing a visual detection
task. Specifically, we present a novel methodology for learning to detect image
transformations visible to human observers through approximating perceptual
thresholds. To do this, we carry out a subjective two-alternative forced-choice
study to estimate perceptual thresholds of human observers detecting local
exposure shifts in images. We then leverage transformation equivariant
representation learning to overcome issues of limited perceptual data. This
representation is then used to train a dense convolutional classifier capable
of detecting local suprathreshold exposure shifts - a distortion common to
image composites. In this context, our model can approximate perceptual
thresholds with an average error of 0.1148 exposure stops between empirical and
predicted thresholds. It can also be trained to detect a range of different
local transformations.
