  In this paper, we present a method using Deep Convolutional Neural Networks
(DCNNs) to detect common glitches in video games. The problem setting consists
of an image (800x800 RGB) as input to be classified into one of five defined
classes, normal image, or one of four different kinds of glitches (stretched,
low resolution, missing and placeholder textures). Using a supervised approach,
we train a ShuffleNetV2 using generated data. This work focuses on detecting
texture graphical anomalies achieving arguably good performance with an
accuracy of 86.8\%, detecting 88\% of the glitches with a false positive rate
of 8.7\%, and with the models being able to generalize and detect glitches even
in unseen objects. We apply a confidence measure as well to tackle the issue
with false positives as well as an effective way of aggregating images to
achieve better detection in production. The main use of this work is the
partial automatization of graphical testing in the final stages of video game
development.
