  Most non-professional photo manipulations are not made using propriety
software like Adobe Photoshop, which is expensive and complicated to use for
the average consumer selfie-taker or meme-maker. Instead, these individuals opt
for user friendly mobile applications like FaceTune and Pixlr to make human
face edits and alterations. Unfortunately, there is no existing dataset to
train a model to classify these type of manipulations. In this paper, we
present a generative model that approximates the distribution of human face
edits and a method for detecting Facetune and Pixlr manipulations to human
faces.
