  This work evaluates the reproducibility of the paper "CNN-generated images
are surprisingly easy to spot... for now" by Wang et al. published at CVPR
2020. The paper addresses the challenge of detecting CNN-generated imagery,
which has reached the potential to even fool humans. The authors propose two
methods which help an image classifier to generalize from being trained on one
specific CNN to detecting imagery produced by unseen architectures, training
methods, or data sets. The paper proposes two methods to help a classifier
generalize: (i) utilizing different kinds of data augmentations and (ii) using
a diverse data set. This report focuses on assessing if these techniques indeed
help the generalization process. Furthermore, we perform additional experiments
to study the limitations of the proposed techniques.
