  We propose using a two-layered deployment of machine learning models to
prevent adversarial attacks. The first layer determines whether the data was
tampered, while the second layer solves a domain-specific problem. We explore
three sets of features and three dataset variations to train machine learning
models. Our results show clustering algorithms achieved promising results. In
particular, we consider the best results were obtained by applying the DBSCAN
algorithm to the structured structural similarity index measure computed
between the images and a white reference image.
