  When there is a suspicious figure reuse case in science, research integrity
investigators often find it difficult to rebut authors claiming that "it
happened by chance". In other words, when there is a "collision" of image
features, it is difficult to justify whether it appears rarely or not. In this
article, we provide a method to predict the rarity of an image feature by
statistically estimating the chance of it randomly occurring across all
scientific imagery. Our method is based on high-dimensional density estimation
of ORB features using 7+ million images in the PubMed Open Access Subset
dataset. We show that this method can lead to meaningful feedback during
research integrity investigations by providing a null hypothesis for scientific
image reuse and thus a p-value during deliberations. We apply the model to a
sample of increasingly complex imagery and confirm that it produces
decreasingly smaller p-values as expected. We discuss applications to research
integrity investigations as well as future work.

Computer Vision and Pattern Recognition;Machine Learning;Machine Learning